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Tuncer T, Dogan S, Baygin M, Barua PD, Palmer EE, March S, Ciaccio EJ, Tan RS, Acharya UR. FLP: Factor lattice pattern-based automated detection of Parkinson's disease and specific language impairment using recorded speech. Comput Biol Med 2024; 173:108280. [PMID: 38547655 DOI: 10.1016/j.compbiomed.2024.108280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/18/2024] [Accepted: 03/09/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND Timely detection of neurodevelopmental and neurological conditions is crucial for early intervention. Specific Language Impairment (SLI) in children and Parkinson's disease (PD) manifests in speech disturbances that may be exploited for diagnostic screening using recorded speech signals. We were motivated to develop an accurate yet computationally lightweight model for speech-based detection of SLI and PD, employing novel feature engineering techniques to mimic the adaptable dynamic weight assignment network capability of deep learning architectures. MATERIALS AND METHODS In this research, we have introduced an advanced feature engineering model incorporating a novel feature extraction function, the Factor Lattice Pattern (FLP), which is a quantum-inspired method and uses a superposition-like mechanism, making it dynamic in nature. The FLP encompasses eight distinct patterns, from which the most appropriate pattern was discerned based on the data structure. Through the implementation of the FLP, we automatically extracted signal-specific textural features. Additionally, we developed a new feature engineering model to assess the efficacy of the FLP. This model is self-organizing, producing nine potential results and subsequently choosing the optimal one. Our speech classification framework consists of (1) feature extraction using the proposed FLP and a statistical feature extractor; (2) feature selection employing iterative neighborhood component analysis and an intersection-based feature selector; (3) classification via support vector machine and k-nearest neighbors; and (4) outcome determination using combinational majority voting to select the most favorable results. RESULTS To validate the classification capabilities of our proposed feature engineering model, designed to automatically detect PD and SLI, we employed three speech datasets of PD and SLI patients. Our presented FLP-centric model achieved classification accuracy of more than 95% and 99.79% for all PD and SLI datasets, respectively. CONCLUSIONS Our results indicate that the proposed model is an accurate alternative to deep learning models in classifying neurological conditions using speech signals.
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Affiliation(s)
- Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey.
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey.
| | - Mehmet Baygin
- Department of Computer Engineering, Faculty of Engineering and Architecture, Erzurum Technical University, Erzurum, Turkey.
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Australia.
| | - Elizabeth Emma Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick, 2031, Australia; School of Women's and Children's Health, University of New South Wales, Randwick, 2031, Australia.
| | - Sonja March
- School of Psychology and Counselling and Centre for Health Research, University of Southern Queensland, Springfield, Australia.
| | - Edward J Ciaccio
- Department of Medicine, Columbia University Irving Medical Center, USA.
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia; Centre for Health Research, University of Southern Queensland, Australia.
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Baygin M, Barua PD, Dogan S, Tuncer T, Hong TJ, March S, Tan RS, Molinari F, Acharya UR. Automated anxiety detection using probabilistic binary pattern with ECG signals. Comput Methods Programs Biomed 2024; 247:108076. [PMID: 38422891 DOI: 10.1016/j.cmpb.2024.108076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/22/2024] [Accepted: 02/09/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND AND AIM Anxiety disorder is common; early diagnosis is crucial for management. Anxiety can induce physiological changes in the brain and heart. We aimed to develop an efficient and accurate handcrafted feature engineering model for automated anxiety detection using ECG signals. MATERIALS AND METHODS We studied open-access electrocardiography (ECG) data of 19 subjects collected via wearable sensors while they were shown videos that might induce anxiety. Using the Hamilton Anxiety Rating Scale, subjects are categorized into normal, light anxiety, moderate anxiety, and severe anxiety groups. ECGs were divided into non-overlapping 4- (Case 1), 5- (Case 2), and 6-second (Case 3) segments for analysis. We proposed a self-organized dynamic pattern-based feature extraction function-probabilistic binary pattern (PBP)-in which patterns within the function were determined by the probabilities of the input signal-dependent values. This was combined with tunable q-factor wavelet transform to facilitate multileveled generation of feature vectors in both spatial and frequency domains. Neighborhood component analysis and Chi2 functions were used to select features and reduce data dimensionality. Shallow k-nearest neighbors and support vector machine classifiers were used to calculate four (=2 × 2) classifier-wise results per input signal. From the latter, novel self-organized combinational majority voting was applied to calculate an additional five voted results. The optimal final model outcome was chosen from among the nine (classifier-wise and voted) results using a greedy algorithm. RESULTS Our model achieved classification accuracies of over 98.5 % for all three cases. Ablation studies confirmed the incremental accuracy of PBP-based feature engineering over traditional local binary pattern feature extraction. CONCLUSIONS The results demonstrated the feasibility and accuracy of our PBP-based feature engineering model for anxiety classification using ECG signals.
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Affiliation(s)
- Mehmet Baygin
- Department of Computer Engineering, Faculty of Engineering and Architecture, Erzurum Technical University, Erzurum, Turkey
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, 23119, Elazig, Turkey.
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, 23119, Elazig, Turkey
| | - Tan Jen Hong
- Data Science and Artificial Intelligence Lab, Singapore General Hospital, Singapore
| | - Sonja March
- Centre for Health Research, University of Southern Queensland, Australia; School of Psychology and Wellbeing, University of Southern Queensland, Australia
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore
| | - Filippo Molinari
- Biolab, PolitoBIOMed Lab, Department of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Turin, Italy
| | - U Rajendra Acharya
- Centre for Health Research, University of Southern Queensland, Australia; School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
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Gudigar A, Kadri NA, Raghavendra U, Samanth J, Maithri M, Inamdar MA, Prabhu MA, Hegde A, Salvi M, Yeong CH, Barua PD, Molinari F, Acharya UR. Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013-2023). Comput Biol Med 2024; 172:108207. [PMID: 38489986 DOI: 10.1016/j.compbiomed.2024.108207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 03/17/2024]
Abstract
Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources.
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Affiliation(s)
- Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Nahrizul Adib Kadri
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
| | - Jyothi Samanth
- Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, 576104, India
| | - M Maithri
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Mahesh Anil Inamdar
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Mukund A Prabhu
- Department of Cardiology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Ajay Hegde
- Manipal Hospitals, Bengaluru, Karnataka, 560102, India
| | - Massimo Salvi
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnicodi Torino, Turin, Italy
| | - Chai Hong Yeong
- School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500, Subang Jaya, Malaysia
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW, 2010, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD, 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnicodi Torino, Turin, Italy
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia; Centre for Health Research, University of Southern Queensland, Toowoomba, QLD, 4350, Australia
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Fallahpoor M, Chakraborty S, Pradhan B, Faust O, Barua PD, Chegeni H, Acharya R. Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space. Comput Methods Programs Biomed 2024; 243:107880. [PMID: 37924769 DOI: 10.1016/j.cmpb.2023.107880] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/16/2023] [Accepted: 10/21/2023] [Indexed: 11/06/2023]
Abstract
Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The success stems from the cohesive information that hybrid PET/CT imaging offers, surpassing the capabilities of individual modalities when used in isolation for different malignancies. However, manual image interpretation requires extensive disease-specific knowledge, and it is a time-consuming aspect of physicians' daily routines. Deep learning algorithms, akin to a practitioner during training, extract knowledge from images to facilitate the diagnosis process by detecting symptoms and enhancing images. This acquired knowledge aids in supporting the diagnosis process through symptom detection and image enhancement. The available review papers on PET/CT imaging have a drawback as they either included additional modalities or examined various types of AI applications. However, there has been a lack of comprehensive investigation specifically focused on the highly specific use of AI, and deep learning, on PET/CT images. This review aims to fill that gap by investigating the characteristics of approaches used in papers that employed deep learning for PET/CT imaging. Within the review, we identified 99 studies published between 2017 and 2022 that applied deep learning to PET/CT images. We also identified the best pre-processing algorithms and the most effective deep learning models reported for PET/CT while highlighting the current limitations. Our review underscores the potential of deep learning (DL) in PET/CT imaging, with successful applications in lesion detection, tumor segmentation, and disease classification in both sinogram and image spaces. Common and specific pre-processing techniques are also discussed. DL algorithms excel at extracting meaningful features, and enhancing accuracy and efficiency in diagnosis. However, limitations arise from the scarcity of annotated datasets and challenges in explainability and uncertainty. Recent DL models, such as attention-based models, generative models, multi-modal models, graph convolutional networks, and transformers, are promising for improving PET/CT studies. Additionally, radiomics has garnered attention for tumor classification and predicting patient outcomes. Ongoing research is crucial to explore new applications and improve the accuracy of DL models in this rapidly evolving field.
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Affiliation(s)
- Maryam Fallahpoor
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Subrata Chakraborty
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia; School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia; Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.
| | - Oliver Faust
- School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, United Kingdom
| | - Prabal Datta Barua
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia
| | | | - Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia
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Kaplan E, Chan WY, Altinsoy HB, Baygin M, Barua PD, Chakraborty S, Dogan S, Tuncer T, Acharya UR. PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI. J Digit Imaging 2023; 36:2441-2460. [PMID: 37537514 PMCID: PMC10584767 DOI: 10.1007/s10278-023-00889-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/18/2023] [Accepted: 07/24/2023] [Indexed: 08/05/2023] Open
Abstract
Detecting neurological abnormalities such as brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI) images is an important research topic in the literature. Numerous machine learning models have been used to detect brain abnormalities accurately. This study addresses the problem of detecting neurological abnormalities in MRI. The motivation behind this problem lies in the need for accurate and efficient methods to assist neurologists in the diagnosis of these disorders. In addition, many deep learning techniques have been applied to MRI to develop accurate brain abnormality detection models, but these networks have high time complexity. Hence, a novel hand-modeled feature-based learning network is presented to reduce the time complexity and obtain high classification performance. The model proposed in this work uses a new feature generation architecture named pyramid and fixed-size patch (PFP). The main aim of the proposed PFP structure is to attain high classification performance using essential feature extractors with both multilevel and local features. Furthermore, the PFP feature extractor generates low- and high-level features using a handcrafted extractor. To obtain the high discriminative feature extraction ability of the PFP, we have used histogram-oriented gradients (HOG); hence, it is named PFP-HOG. Furthermore, the iterative Chi2 (IChi2) is utilized to choose the clinically significant features. Finally, the k-nearest neighbors (kNN) with tenfold cross-validation is used for automated classification. Four MRI neurological databases (AD dataset, brain tumor dataset 1, brain tumor dataset 2, and merged dataset) have been utilized to develop our model. PFP-HOG and IChi2-based models attained 100%, 94.98%, 98.19%, and 97.80% using the AD dataset, brain tumor dataset1, brain tumor dataset 2, and merged brain MRI dataset, respectively. These findings not only provide an accurate and robust classification of various neurological disorders using MRI but also hold the potential to assist neurologists in validating manual MRI brain abnormality screening.
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Affiliation(s)
- Ela Kaplan
- Department of Radiology, Elazig Fethi Sekin City Hospital, Elazig, Turkey
| | - Wai Yee Chan
- Imaging Department, Gleneagles Hospital Kuala Lumpur, Jalan Ampang, Kampung Berembang, 50450, Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia
| | - Hasan Baki Altinsoy
- Department of Radiology, Faculty of Medicine, Duzce University, Duzce, Turkey
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Erzurum Technical University, Erzurum, Turkey
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Springfield, Australia
| | - Subrata Chakraborty
- Faculty of Science, Agriculture, Business and Law, School of Science and Technology, University of New England, Armidale, NSW, 2351, Australia
- Center for Advanced Modelling and Geospatial Information Systems, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey.
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
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Loh HW, Ooi CP, Oh SL, Barua PD, Tan YR, Molinari F, March S, Acharya UR, Fung DSS. Deep neural network technique for automated detection of ADHD and CD using ECG signal. Comput Methods Programs Biomed 2023; 241:107775. [PMID: 37651817 DOI: 10.1016/j.cmpb.2023.107775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 08/09/2023] [Accepted: 08/22/2023] [Indexed: 09/02/2023]
Abstract
BACKGROUND AND OBJECTIVE Attention Deficit Hyperactivity problem (ADHD) is a common neurodevelopment problem in children and adolescents that can lead to long-term challenges in life outcomes if left untreated. Also, ADHD is frequently associated with Conduct Disorder (CD), and multiple research have found similarities in clinical signs and behavioral symptoms between both diseases, making differentiation between ADHD, ADHD comorbid with CD (ADHD+CD), and CD a subjective diagnosis. Therefore, the goal of this pilot study is to create the first explainable deep learning (DL) model for objective ECG-based ADHD/CD diagnosis as having an objective biomarker may improve diagnostic accuracy. METHODS The dataset used in this study consist of ECG data collected from 45 ADHD, 62 ADHD+CD, and 16 CD patients at the Child Guidance Clinic in Singapore. The ECG data were segmented into 2 s epochs and directly used to train our 1-dimensional (1D) convolutional neural network (CNN) model. RESULTS The proposed model yielded 96.04% classification accuracy, 96.26% precision, 95.99% sensitivity, and 96.11% F1-score. The Gradient-weighted class activation mapping (Grad-CAM) function was also used to highlight the important ECG characteristics at specific time points that most impact the classification score. CONCLUSION In addition to achieving model performance results with our suggested DL method, Grad-CAM's implementation also offers vital temporal data that clinicians and other mental healthcare professionals can use to make wise medical judgments. We hope that by conducting this pilot study, we will be able to encourage larger-scale research with a larger biosignal dataset. Hence allowing biosignal-based computer-aided diagnostic (CAD) tools to be implemented in healthcare and ambulatory settings, as ECG can be easily obtained via wearable devices such as smartwatches.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Shu Lih Oh
- Cogninet Australia, Sydney, NSW 2010, Australia
| | - Prabal Datta Barua
- Cogninet Australia, Sydney, NSW 2010, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia; School of Business (Information System), University of Southern Queensland, Australia; Australian International Institute of Higher Education, Sydney, NSW 2000, Australia; School of Science & Technology, University of New England, Australia; School of Biosciences, Taylor's University, Malaysia; School of Computing, SRM Institute of Science and Technology, India; School of Science and Technology, Kumamoto University, Japan; Sydney School of Education and Social work, University of Sydney, Australia
| | - Yi Ren Tan
- Developmental Psychiatry, Institute of Mental Health, Singapore
| | - Filippo Molinari
- Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
| | - Sonja March
- Centre for Health Research and School of Psychology and Wellbeing, University of Southern Queensland, Springfield, Australia
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, Australia.
| | - Daniel Shuen Sheng Fung
- Developmental Psychiatry, Institute of Mental Health, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, DUKE NUS Medical School, Yong Loo Lin School of Medicine, National University of Singapore
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Xu S, Deo RC, Soar J, Barua PD, Faust O, Homaira N, Jaffe A, Kabir AL, Acharya UR. Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022). Comput Methods Programs Biomed 2023; 241:107746. [PMID: 37660550 DOI: 10.1016/j.cmpb.2023.107746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 07/07/2023] [Accepted: 08/02/2023] [Indexed: 09/05/2023]
Abstract
BACKGROUND AND OBJECTIVE Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality. METHODS We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance. RESULTS We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent. CONCLUSIONS Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.
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Affiliation(s)
- Shuting Xu
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia; Cogninet Australia, Sydney, NSW 2010, Australia
| | - Ravinesh C Deo
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia
| | - Jeffrey Soar
- School of Business, University of Southern Queensland, Australia
| | - Prabal Datta Barua
- Cogninet Australia, Sydney, NSW 2010, Australia; School of Business, University of Southern Queensland, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia; Australian International Institute of Higher Education, Sydney, NSW 2000, Australia; School of Science Technology, University of New England, Australia; School of Biosciences, Taylor's University, Malaysia; School of Computing, SRM Institute of Science and Technology, India; School of Science and Technology, Kumamoto University, Japan; Sydney School of Education and Social Work, University of Sydney, Australia.
| | - Oliver Faust
- School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, UK
| | - Nusrat Homaira
- School of Clinical Medicine, University of New South Wales, Australia; Sydney Children's Hospital, Sydney, Australia; James P. Grant School of Public Health, Dhaka, Bangladesh
| | - Adam Jaffe
- School of Clinical Medicine, University of New South Wales, Australia; Sydney Children's Hospital, Sydney, Australia
| | | | - U Rajendra Acharya
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia; School of Science and Technology, Kumamoto University, Japan
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Seoni S, Jahmunah V, Salvi M, Barua PD, Molinari F, Acharya UR. Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013-2023). Comput Biol Med 2023; 165:107441. [PMID: 37683529 DOI: 10.1016/j.compbiomed.2023.107441] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023]
Abstract
Uncertainty estimation in healthcare involves quantifying and understanding the inherent uncertainty or variability associated with medical predictions, diagnoses, and treatment outcomes. In this era of Artificial Intelligence (AI) models, uncertainty estimation becomes vital to ensure safe decision-making in the medical field. Therefore, this review focuses on the application of uncertainty techniques to machine and deep learning models in healthcare. A systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our analysis revealed that Bayesian methods were the predominant technique for uncertainty quantification in machine learning models, with Fuzzy systems being the second most used approach. Regarding deep learning models, Bayesian methods emerged as the most prevalent approach, finding application in nearly all aspects of medical imaging. Most of the studies reported in this paper focused on medical images, highlighting the prevalent application of uncertainty quantification techniques using deep learning models compared to machine learning models. Interestingly, we observed a scarcity of studies applying uncertainty quantification to physiological signals. Thus, future research on uncertainty quantification should prioritize investigating the application of these techniques to physiological signals. Overall, our review highlights the significance of integrating uncertainty techniques in healthcare applications of machine learning and deep learning models. This can provide valuable insights and practical solutions to manage uncertainty in real-world medical data, ultimately improving the accuracy and reliability of medical diagnoses and treatment recommendations.
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Affiliation(s)
- Silvia Seoni
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | | | - Massimo Salvi
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Toowoomba, QLD, 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
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Raghavendra U, Gudigar A, Paul A, Goutham TS, Inamdar MA, Hegde A, Devi A, Ooi CP, Deo RC, Barua PD, Molinari F, Ciaccio EJ, Acharya UR. Brain tumor detection and screening using artificial intelligence techniques: Current trends and future perspectives. Comput Biol Med 2023; 163:107063. [PMID: 37329621 DOI: 10.1016/j.compbiomed.2023.107063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 05/16/2023] [Accepted: 05/19/2023] [Indexed: 06/19/2023]
Abstract
A brain tumor is an abnormal mass of tissue located inside the skull. In addition to putting pressure on the healthy parts of the brain, it can lead to significant health problems. Depending on the region of the brain tumor, it can cause a wide range of health issues. As malignant brain tumors grow rapidly, the mortality rate of individuals with this cancer can increase substantially with each passing week. Hence it is vital to detect these tumors early so that preventive measures can be taken at the initial stages. Computer-aided diagnostic (CAD) systems, in coordination with artificial intelligence (AI) techniques, have a vital role in the early detection of this disorder. In this review, we studied 124 research articles published from 2000 to 2022. Here, the challenges faced by CAD systems based on different modalities are highlighted along with the current requirements of this domain and future prospects in this area of research.
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Affiliation(s)
- U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India.
| | - Aritra Paul
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - T S Goutham
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Mahesh Anil Inamdar
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India
| | - Ajay Hegde
- Consultant Neurosurgeon Manipal Hospitals, Sarjapur Road, Bangalore, India
| | - Aruna Devi
- School of Education and Tertiary Access, University of the Sunshine Coast, Caboolture Campus, Australia
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore, 599494, Singapore
| | - Ravinesh C Deo
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW, 2010, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD, 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, 10129, Torino, Italy
| | - Edward J Ciaccio
- Department of Medicine, Columbia University Medical Center, New York, NY, 10032, USA
| | - U Rajendra Acharya
- School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, 860-8555, Japan
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Atila O, Deniz E, Ari A, Sengur A, Chakraborty S, Barua PD, Acharya UR. LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain's Primes on Ulam's Spiral-Based Features with Electroencephalogram Signals. Sensors (Basel) 2023; 23:7032. [PMID: 37631569 PMCID: PMC10459515 DOI: 10.3390/s23167032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/27/2023] [Accepted: 08/05/2023] [Indexed: 08/27/2023]
Abstract
Anxiety, learning disabilities, and depression are the symptoms of attention deficit hyperactivity disorder (ADHD), an isogenous pattern of hyperactivity, impulsivity, and inattention. For the early diagnosis of ADHD, electroencephalogram (EEG) signals are widely used. However, the direct analysis of an EEG is highly challenging as it is time-consuming, nonlinear, and nonstationary in nature. Thus, in this paper, a novel approach (LSGP-USFNet) is developed based on the patterns obtained from Ulam's spiral and Sophia Germain's prime numbers. The EEG signals are initially filtered to remove the noise and segmented with a non-overlapping sliding window of a length of 512 samples. Then, a time-frequency analysis approach, namely continuous wavelet transform, is applied to each channel of the segmented EEG signal to interpret it in the time and frequency domain. The obtained time-frequency representation is saved as a time-frequency image, and a non-overlapping n × n sliding window is applied to this image for patch extraction. An n × n Ulam's spiral is localized on each patch, and the gray levels are acquired from this patch as features where Sophie Germain's primes are located in Ulam's spiral. All gray tones from all patches are concatenated to construct the features for ADHD and normal classes. A gray tone selection algorithm, namely ReliefF, is employed on the representative features to acquire the final most important gray tones. The support vector machine classifier is used with a 10-fold cross-validation criteria. Our proposed approach, LSGP-USFNet, was developed using a publicly available dataset and obtained an accuracy of 97.46% in detecting ADHD automatically. Our generated model is ready to be validated using a bigger database and it can also be used to detect other children's neurological disorders.
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Affiliation(s)
- Orhan Atila
- Electrical-Electronics Engineering Department, Technology Faculty, Firat University, 23119 Elazig, Turkey; (O.A.); (E.D.); (A.S.)
| | - Erkan Deniz
- Electrical-Electronics Engineering Department, Technology Faculty, Firat University, 23119 Elazig, Turkey; (O.A.); (E.D.); (A.S.)
| | - Ali Ari
- Computer Engineering Department, Engineering Faculty, Inonu University, 44280 Malatya, Turkey;
| | - Abdulkadir Sengur
- Electrical-Electronics Engineering Department, Technology Faculty, Firat University, 23119 Elazig, Turkey; (O.A.); (E.D.); (A.S.)
| | - Subrata Chakraborty
- Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Prabal Datta Barua
- Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
- School of Information Systems, University of Southern Queensland, Springfield, QLD 4300, Australia
| | - U. Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia;
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11
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Horry MJ, Chakraborty S, Pradhan B, Paul M, Zhu J, Loh HW, Barua PD, Acharya UR. Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models. Sensors (Basel) 2023; 23:6585. [PMID: 37514877 PMCID: PMC10385599 DOI: 10.3390/s23146585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023]
Abstract
Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening.
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Affiliation(s)
- Michael J Horry
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
- IBM Australia Limited, Sydney, NSW 2000, Australia
| | - Subrata Chakraborty
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
- Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
| | - Biswajeet Pradhan
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
- Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Manoranjan Paul
- Machine Vision and Digital Health (MaViDH), School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW 2795, Australia
| | - Jing Zhu
- Department of Radiology, Westmead Hospital, Westmead, NSW 2145, Australia
| | - Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
| | - Prabal Datta Barua
- Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
- Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia
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12
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Muezzinoglu T, Baygin N, Tuncer I, Barua PD, Baygin M, Dogan S, Tuncer T, Palmer EE, Cheong KH, Acharya UR. PatchResNet: Multiple Patch Division-Based Deep Feature Fusion Framework for Brain Tumor Classification Using MRI Images. J Digit Imaging 2023; 36:973-987. [PMID: 36797543 PMCID: PMC10287865 DOI: 10.1007/s10278-023-00789-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 02/18/2023] Open
Abstract
Modern computer vision algorithms are based on convolutional neural networks (CNNs), and both end-to-end learning and transfer learning modes have been used with CNN for image classification. Thus, automated brain tumor classification models have been proposed by deploying CNNs to help medical professionals. Our primary objective is to increase the classification performance using CNN. Therefore, a patch-based deep feature engineering model has been proposed in this work. Nowadays, patch division techniques have been used to attain high classification performance, and variable-sized patches have achieved good results. In this work, we have used three types of patches of different sizes (32 × 32, 56 × 56, 112 × 112). Six feature vectors have been obtained using these patches and two layers of the pretrained ResNet50 (global average pooling and fully connected layers). In the feature selection phase, three selectors-neighborhood component analysis (NCA), Chi2, and ReliefF-have been used, and 18 final feature vectors have been obtained. By deploying k nearest neighbors (kNN), 18 results have been calculated. Iterative hard majority voting (IHMV) has been applied to compute the general classification accuracy of this framework. This model uses different patches, feature extractors (two layers of the ResNet50 have been utilized as feature extractors), and selectors, making this a framework that we have named PatchResNet. A public brain image dataset containing four classes (glioblastoma multiforme (GBM), meningioma, pituitary tumor, healthy) has been used to develop the proposed PatchResNet model. Our proposed PatchResNet attained 98.10% classification accuracy using the public brain tumor image dataset. The developed PatchResNet model obtained high classification accuracy and has the advantage of being a self-organized framework. Therefore, the proposed method can choose the best result validation prediction vectors and achieve high image classification performance.
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Affiliation(s)
- Taha Muezzinoglu
- Department of Computer Engineering, Faculty of Engineering, Munzur University, Tunceli, Turkey
| | - Nursena Baygin
- Department of Computer Engineering, Faculty of Engineering, Erzurum Technical University, Erzurum, Turkey
| | | | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia
| | - Mehmet Baygin
- Department of Computer Engineering, Faculty of Engineering, Ardahan University, Ardahan, Turkey
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Elizabeth Emma Palmer
- Centre of Clinical Genetics, Sydney Children’s Hospitals Network, Randwick, 2031 Australia
- School of Women’s and Children’s Health, University of New South Wales, Randwick, 2031 Australia
| | - Kang Hao Cheong
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore, S487372 Singapore
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489 Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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13
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Sut SK, Koc M, Zorlu G, Serhatlioglu I, Barua PD, Dogan S, Baygin M, Tuncer T, Tan RS, Acharya UR. Automated Adrenal Gland Disease Classes Using Patch-Based Center Symmetric Local Binary Pattern Technique with CT Images. J Digit Imaging 2023; 36:879-892. [PMID: 36658376 PMCID: PMC10287607 DOI: 10.1007/s10278-022-00759-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 12/09/2022] [Accepted: 12/13/2022] [Indexed: 01/21/2023] Open
Abstract
Incidental adrenal masses are seen in 5% of abdominal computed tomography (CT) examinations. Accurate discrimination of the possible differential diagnoses has important therapeutic and prognostic significance. A new handcrafted machine learning method has been developed for the automated and accurate classification of adrenal gland CT images. A new dataset comprising 759 adrenal gland CT image slices from 96 subjects were analyzed. Experts had labeled the collected images into four classes: normal, pheochromocytoma, lipid-poor adenoma, and metastasis. The images were preprocessed, resized, and the image features were extracted using the center symmetric local binary pattern (CS-LBP) method. CT images were next divided into 16 × 16 fixed-size patches, and further feature extraction using CS-LBP was performed on these patches. Next, extracted features were selected using neighborhood component analysis (NCA) to obtain the most meaningful ones for downstream classification. Finally, the selected features were classified using k-nearest neighbor (kNN), support vector machine (SVM), and neural network (NN) classifiers to obtain the optimum performing model. Our proposed method obtained an accuracy of 99.87%, 99.21%, and 98.81% with kNN, SVM, and NN classifiers, respectively. Hence, the kNN classifier yielded the highest classification results with no pathological image misclassified as normal. Our developed fixed patch CS-LBP-based automatic classification of adrenal gland pathologies on CT images is highly accurate and has low time complexity [Formula: see text]. It has the potential to be used for screening of adrenal gland disease classes with CT images.
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Affiliation(s)
- Suat Kamil Sut
- Department of Radiology, Adiyaman Training and Research Hospital, Adiyaman, Turkey
| | - Mustafa Koc
- Department of Radiology, Faculty of Medicine, Firat University, Elazig, Turkey
| | - Gokhan Zorlu
- Department of Biophysics, Faculty of Medicine, Firat University, Elazig, Turkey
| | - Ihsan Serhatlioglu
- Department of Biophysics, Faculty of Medicine, Firat University, Elazig, Turkey
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350 Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007 Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489 Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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14
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Baygin M, Barua PD, Chakraborty S, Tuncer I, Dogan S, Palmer E, Tuncer T, Kamath AP, Ciaccio EJ, Acharya UR. CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals. Physiol Meas 2023; 44. [PMID: 36599170 DOI: 10.1088/1361-6579/acb03c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 01/04/2023] [Indexed: 01/05/2023]
Abstract
Objective.Schizophrenia (SZ) is a severe, chronic psychiatric-cognitive disorder. The primary objective of this work is to present a handcrafted model using state-of-the-art technique to detect SZ accurately with EEG signals.Approach.In our proposed work, the features are generated using a histogram-based generator and an iterative decomposition model. The graph-based molecular structure of the carbon chain is employed to generate low-level features. Hence, the developed feature generation model is called the carbon chain pattern (CCP). An iterative tunable q-factor wavelet transform (ITQWT) technique is implemented in the feature extraction phase to generate various sub-bands of the EEG signal. The CCP was applied to the generated sub-bands to obtain several feature vectors. The clinically significant features were selected using iterative neighborhood component analysis (INCA). The selected features were then classified using the k nearest neighbor (kNN) with a 10-fold cross-validation strategy. Finally, the iterative weighted majority method was used to obtain the results in multiple channels.Main results.The presented CCP-ITQWT and INCA-based automated model achieved an accuracy of 95.84% and 99.20% using a single channel and majority voting method, respectively with kNN classifier.Significance.Our results highlight the success of the proposed CCP-ITQWT and INCA-based model in the automated detection of SZ using EEG signals.
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Affiliation(s)
- Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Australia.,Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
| | - Subrata Chakraborty
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW, 2351, Australia.,Center for Advanced Modelling and Geospatial Information Systems, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Ilknur Tuncer
- Elazig Governorship, Interior Ministry, Elazig, Turkey
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Elizabeth Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick 2031, Australia.,School of Women's and Children's Health, University of New South Wales, Randwick 2031, Australia
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Aditya P Kamath
- Biomedical Engineering, Brown University, Providence, RI, United States of America
| | - Edward J Ciaccio
- Department of Medicine, Columbia University Irving Medical Center, United States of America
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, S599489, Singapore.,Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore.,Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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15
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Hossain E, Rana R, Higgins N, Soar J, Barua PD, Pisani AR, Turner K. Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review. Comput Biol Med 2023; 155:106649. [PMID: 36805219 DOI: 10.1016/j.compbiomed.2023.106649] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 01/04/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023]
Abstract
BACKGROUND Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. METHODOLOGY After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: (1) medical note classification, (2) clinical entity recognition, (3) text summarisation, (4) deep learning (DL) and transfer learning architecture, (5) information extraction, (6) Medical language translation and (7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULT AND DISCUSSION EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. CONCLUSION We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification.
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Affiliation(s)
- Elias Hossain
- School of Engineering & Physical Sciences, North South University, Dhaka 1229, Bangladesh.
| | - Rajib Rana
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield Central QLD 4300, Australia
| | - Niall Higgins
- School of Management and Enterprise, University of Southern Queensland, Darling Heights QLD 4350, Australia; School of Nursing, Queensland University of Technology, Kelvin Grove, Brisbane, QLD 4000, Australia; Metro North Mental Health, Herston QLD 4029, Australia
| | - Jeffrey Soar
- School of Business, University of Southern Queensland, Springfield Central QLD 4300, Australia
| | - Prabal Datta Barua
- School of Business, University of Southern Queensland, Springfield Central QLD 4300, Australia
| | - Anthony R Pisani
- Center for the Study and Prevention of Suicide, University of Rochester, Rochester, NY, United States
| | - Kathryn Turner
- School of Nursing, Queensland University of Technology, Kelvin Grove, Brisbane, QLD 4000, Australia
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16
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Faust O, De Michele S, Koh JE, Jahmunah V, Lih OS, Kamath AP, Barua PD, Ciaccio EJ, Lewis SK, Green PH, Bhagat G, Acharya UR. Automated analysis of small intestinal lamina propria to distinguish normal, Celiac Disease, and Non-Celiac Duodenitis biopsy images. Comput Methods Programs Biomed 2023; 230:107320. [PMID: 36608429 DOI: 10.1016/j.cmpb.2022.107320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 12/16/2022] [Accepted: 12/18/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Celiac Disease (CD) is characterized by gluten intolerance in genetically predisposed individuals. High disease prevalence, absence of a cure, and low diagnosis rates make this disease a public health problem. The diagnosis of CD predominantly relies on recognizing characteristic mucosal alterations of the small intestine, such as villous atrophy, crypt hyperplasia, and intraepithelial lymphocytosis. However, these changes are not entirely specific to CD and overlap with Non-Celiac Duodenitis (NCD) due to various etiologies. We investigated whether Artificial Intelligence (AI) models could assist in distinguishing normal, CD, and NCD (and unaffected individuals) based on the characteristics of small intestinal lamina propria (LP). METHODS Our method was developed using a dataset comprising high magnification biopsy images of the duodenal LP compartment of CD patients with different clinical stages of CD, those with NCD, and individuals lacking an intestinal inflammatory disorder (controls). A pre-processing step was used to standardize and enhance the acquired images. RESULTS For the normal controls versus CD use case, a Support Vector Machine (SVM) achieved an Accuracy (ACC) of 98.53%. For a second use case, we investigated the ability of the classification algorithm to differentiate between normal controls and NCD. In this use case, the SVM algorithm with linear kernel outperformed all the tested classifiers by achieving 98.55% ACC. CONCLUSIONS To the best of our knowledge, this is the first study that documents automated differentiation between normal, NCD, and CD biopsy images. These findings are a stepping stone toward automated biopsy image analysis that can significantly benefit patients and healthcare providers.
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Affiliation(s)
| | - Simona De Michele
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, USA
| | - Joel Ew Koh
- Department of Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - V Jahmunah
- Department of Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Oh Shu Lih
- Department of Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | | | - Prabal Datta Barua
- Cogninet Australia, Sydney, NSW 2010, Australia; School of Management & Enterprise, University of Southern Queensland, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Edward J Ciaccio
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - Suzanne K Lewis
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - Peter H Green
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - Govind Bhagat
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, USA; Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, USA
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, 599494, Singapore; Department of Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.
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17
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Tuncer I, Barua PD, Dogan S, Baygin M, Tuncer T, Tan RS, Yeong CH, Acharya UR. Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography. Inform Med Unlocked 2023; 36:101158. [PMID: 36618887 PMCID: PMC9804964 DOI: 10.1016/j.imu.2022.101158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 12/30/2022] [Accepted: 12/30/2022] [Indexed: 01/01/2023] Open
Abstract
Background Chest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering. Material and method We used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm. Results Swin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity. Conclusions Our handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate that our presented swin-textural is a self-organized, highly accurate, and lightweight image classification model and is better than the compared deep learning models for this dataset.
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Affiliation(s)
- Ilknur Tuncer
- Elazig Governorship, Interior Ministry, Elazig, Turkey
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Toowoomba, QLD, 4350, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Mehmet Baygin
- Department of Computer Engineering, Faculty of Engineering, Ardahan University, Ardahan, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore
- Duke-NUS Medical School, Singapore
| | - Chai Hong Yeong
- School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500, Subang Jaya, Malaysia
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Barua PD, Aydemir E, Dogan S, Erten M, Kaysi F, Tuncer T, Fujita H, Palmer E, Acharya UR. Novel favipiravir pattern-based learning model for automated detection of specific language impairment disorder using vowels. Neural Comput Appl 2023; 35:6065-6077. [PMID: 36408288 PMCID: PMC9660223 DOI: 10.1007/s00521-022-07999-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 10/25/2022] [Indexed: 11/14/2022]
Abstract
Specific language impairment (SLI) is one of the most common diseases in children, and early diagnosis can help to obtain better timely therapy economically. It is difficult and time-consuming for clinicians to accurately detect SLI through standard clinical assessments. Hence, machine learning algorithms have been developed to assist in the accurate diagnosis of SLI. This work aims to investigate the graph of the favipiravir molecule-based feature extraction function and propose an accurate SLI detection model using vowels. We proposed a novel handcrafted machine learning framework. This architecture comprises the favipiravir molecular structure pattern, statistical feature extractor, wavelet packet decomposition (WPD), iterative neighborhood component analysis (INCA), and support vector machine (SVM) classifier. Two feature extraction models, statistical and textural, are employed in the handcrafted feature generation methodology. A new nature-inspired graph-based feature extractor that uses the chemical depiction of the favipiravir (favipiravir became popular with the COVID-19 pandemic) is employed for feature extraction. Finally, the proposed favipiravir pattern, statistical feature extractor, and wavelet packet decomposition are used to create a feature vector. Moreover, a statistical feature extractor is used in this work. The WPD generates multilevel features, and the most meaningful features are selected using the NCA feature selector. Finally, these chosen features are fed to SVM classifier for automated classification. Two validation methods, (i) leave one subject out (LOSO) and (ii) tenfold cross-validations (CV), are used to obtain robust classification results. Our proposed favipiravir pattern-based model developed using a vowel dataset can detect SLI children with an accuracy of 99.87% and 98.86% using tenfold and LOSO CV strategies, respectively. These results demonstrated the high vowel classification ability of the proposed favipiravir pattern-based model.
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Affiliation(s)
- Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350 Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007 Australia
| | - Emrah Aydemir
- Department of Management Information Systems, Management Faculty, Sakarya University, Sakarya, Turkey
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Mehmet Erten
- Laboratory of Medical Biochemistry, Public Health Lab., Malatya, Turkey
| | - Feyzi Kaysi
- Vocational School of Technical Sciences, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Hamido Fujita
- Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Viet Nam
- Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, Granada, Spain
- Regional Research Center, Iwate Prefectural University, Iwate, Japan
| | - Elizabeth Palmer
- Centre of Clinical Genetics, Sydney Children’s Hospitals Network, Randwick, 2031 Australia
- Discipline of Paediatrics and Child Health, School of Clinical Medicine Randwick, Faculty of Medicine and Health, UNSW, Randwick, NSW 2031 Australia
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489 Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Barua PD, Keles T, Dogan S, Baygin M, Tuncer T, Demir CF, Fujita H, Tan RS, Ooi CP, Rajendra Acharya U. Automated EEG sentence classification using novel dynamic-sized binary pattern and multilevel discrete wavelet transform techniques with TSEEG database. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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20
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Erten M, Acharya MR, Kamath AP, Sampathila N, Bairy GM, Aydemir E, Barua PD, Baygin M, Tuncer I, Dogan S, Tuncer T. Hamlet-Pattern-Based Automated COVID-19 and Influenza Detection Model Using Protein Sequences. Diagnostics (Basel) 2022; 12:diagnostics12123181. [PMID: 36553188 PMCID: PMC9777168 DOI: 10.3390/diagnostics12123181] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/08/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
SARS-CoV-2 and Influenza-A can present similar symptoms. Computer-aided diagnosis can help facilitate screening for the two conditions, and may be especially relevant and useful in the current COVID-19 pandemic because seasonal Influenza-A infection can still occur. We have developed a novel text-based classification model for discriminating between the two conditions using protein sequences of varying lengths. We downloaded viral protein sequences of SARS-CoV-2 and Influenza-A with varying lengths (all 100 or greater) from the NCBI database and randomly selected 16,901 SARS-CoV-2 and 19,523 Influenza-A sequences to form a two-class study dataset. We used a new feature extraction function based on a unique pattern, HamletPat, generated from the text of Shakespeare's Hamlet, and a signum function to extract local binary pattern-like bits from overlapping fixed-length (27) blocks of the protein sequences. The bits were converted to decimal map signals from which histograms were extracted and concatenated to form a final feature vector of length 1280. The iterative Chi-square function selected the 340 most discriminative features to feed to an SVM with a Gaussian kernel for classification. The model attained 99.92% and 99.87% classification accuracy rates using hold-out (75:25 split ratio) and five-fold cross-validations, respectively. The excellent performance of the lightweight, handcrafted HamletPat-based classification model suggests that it can be a valuable tool for screening protein sequences to discriminate between SARS-CoV-2 and Influenza-A infections.
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Affiliation(s)
- Mehmet Erten
- Laboratory of Medical Biochemistry, Malatya Training and Research Hospital, 44000 Malatya, Turkey
| | - Madhav R Acharya
- Department of Biomedical Engineering, Manipal Academy of Higher Education, Manipal 04478, India
| | - Aditya P Kamath
- Center for Biomedical Engineering, Brown University, Providence, RI 02912, USA
| | - Niranjana Sampathila
- Department of Biomedical Engineering, Manipal Academy of Higher Education, Manipal 04478, India
| | - G Muralidhar Bairy
- Department of Biomedical Engineering, Manipal Academy of Higher Education, Manipal 04478, India
| | - Emrah Aydemir
- Department of Management Information, College of Management, Sakarya University, 54050 Sakarya, Turkey
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Mehmet Baygin
- Department of Computer Engineering, Faculty of Engineering, Ardahan University, 75000 Ardahan, Turkey
| | - Ilknur Tuncer
- Elazig Governorship, Interior Ministry, 23119 Elazig, Turkey
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, 23119 Elazig, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, 23119 Elazig, Turkey
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Key S, Demir S, Gurger M, Yilmaz E, Barua PD, Dogan S, Tuncer T, Arunkumar N, Tan RS, Acharya UR. ViVGG19: Novel exemplar deep feature extraction-based shoulder rotator cuff tear and biceps tendinosis detection using magnetic resonance images. Med Eng Phys 2022; 110:103864. [PMID: 35987726 DOI: 10.1016/j.medengphy.2022.103864] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 07/07/2022] [Accepted: 07/30/2022] [Indexed: 01/18/2023]
Abstract
BACKGROUND AND PURPOSE Rotator cuff tear (RCT) and biceps tendinosis (BT) are the two most common shoulder disorders worldwide. These disorders can be diagnosed using magnetic resonance imaging (MRI), but the expert interpretation is manual, time-consuming, and subjected to human errors. Therefore, a fixed-size feature extraction model was created to objectively and accurately perform automated binary classification of RCT vs. normal and BT vs. normal on MRI images. MATERIALS AND METHODS We have developed an exemplar deep feature extraction model to diagnose RCT and BT disorders. The model was tested on a new MR image dataset comprising transverse, sagittal, and coronal MRI images of the shoulder that had been organized into three cases. BT was studied on transverse MRI images (Case 1), while RCT was studied on sagittal (Case 2) and coronal MRI images (Case 3). Our model comprised deep feature generation using a pre-trained VGG19, feature selection using iterative neighborhood component analysis (INCA), and classification using shallow standard classifiers k-nearest neighbors (KNN), support vector machine (SVM), and artificial neural network (ANN). In the feature extraction phase, two fully connected layers were used to extract deep features from the original image, and sixteen fixed-size patches obtained by the division of the original image. This model was named Vision VGG19 (ViVGG), analogous to vision transformers (ViT). The feature vector is extracted from the raw image dataset, and 16 feature vectors are extracted from each fixed-size patch. Seventeen feature vectors obtained from each image are obtained from fc6 and fc7 layers of the pre-trained VGG19, are merged to obtain final feature vector. INCA was used to choose the top features from the created features, and the chosen features were classified using shallow classifiers. RESULTS We defined three cases to evaluate the proposed ViVGG19 to diagnose RT and BCT disorders. Our proposed ViVGG19 model achieved more than 99% accuracy using the KNN classifier. CONCLUSIONS ViVGG19 is a very effective model for detecting RCT and BT disorders on shoulder MRI images. The developed automated system is ready to be tested with a bigger diverse database obtained from different medical centers.
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Affiliation(s)
- Sefa Key
- Department of Orthopedics, Bingol State Hospital, Ministry of Health, Bingol, Turkey
| | - Sukru Demir
- Department of Orthopedics, Firat University Hospital, Firat University, Elazig, 23119, Turkey
| | - Murat Gurger
- Department of Orthopedics, Firat University Hospital, Firat University, Elazig, 23119, Turkey
| | - Erhan Yilmaz
- Department of Orthopedics, Firat University Hospital, Firat University, Elazig, 23119, Turkey
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey.
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - N Arunkumar
- Rathinam College of Engineering, Coimbatore, India
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia, University, Taichung, Taiwan
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Kuluozturk M, Kobat MA, Barua PD, Dogan S, Tuncer T, Tan RS, Ciaccio EJ, Acharya UR. DKPNet41: Directed knight pattern network-based cough sound classification model for automatic disease diagnosis. Med Eng Phys 2022; 110:103870. [PMID: 35989223 PMCID: PMC9356574 DOI: 10.1016/j.medengphy.2022.103870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 08/03/2022] [Accepted: 08/05/2022] [Indexed: 01/18/2023]
Abstract
PROBLEM Cough-based disease detection is a hot research topic for machine learning, and much research has been published on the automatic detection of Covid-19. However, these studies are useful for the diagnosis of different diseases. AIM In this work, we collected a new and large (n=642 subjects) cough sound dataset comprising four diagnostic categories: 'Covid-19', 'heart failure', 'acute asthma', and 'healthy', and used it to train, validate, and test a novel model designed for automatic detection. METHOD The model consists of four main components: novel feature generation based on a specifically directed knight pattern (DKP), signal decomposition using four pooling methods, feature selection using iterative neighborhood analysis (INCA), and classification using the k-nearest neighbor (kNN) classifier with ten-fold cross-validation. Multilevel multiple pooling decomposition combined with DKP yielded 41 feature vectors (40 extracted plus one original cough sound). From these, the ten best feature vectors were selected. Based on each vector's misclassification rate, redundant feature vectors were eliminated and then merged. The merged vector's most informative features automatically selected using INCA were input to a standard kNN classifier. RESULTS The model, called DKPNet41, attained a high accuracy of 99.39% for cough sound-based multiclass classification of the four categories. CONCLUSIONS The results obtained in the study showed that the DKPNet41 model automatically and efficiently classifies cough sounds for disease diagnosis.
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Affiliation(s)
- Mutlu Kuluozturk
- Department of Pulmonology, Firat University Hospital, Elazig, Turkey
| | - Mehmet Ali Kobat
- Department of Cardiology, Firat University Hospital, Elazig, Turkey
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey.
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore
| | - Edward J Ciaccio
- Department of Medicine, Columbia University Irving Medical Center, USA
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Tasci G, Loh HW, Barua PD, Baygin M, Tasci B, Dogan S, Tuncer T, Palmer EE, Tan RS, Acharya UR. Automated accurate detection of depression using twin Pascal’s triangles lattice pattern with EEG Signals. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Barua PD, Vicnesh J, Lih OS, Palmer EE, Yamakawa T, Kobayashi M, Acharya UR. Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review. Cogn Neurodyn 2022:1-22. [PMID: 36467993 PMCID: PMC9684805 DOI: 10.1007/s11571-022-09904-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 09/26/2022] [Accepted: 10/17/2022] [Indexed: 11/24/2022] Open
Abstract
Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren't any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts.
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Affiliation(s)
- Prabal Datta Barua
- School of Management and Enterprise, University of Southern Queensland, Springfield, Australia
| | - Jahmunah Vicnesh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Oh Shu Lih
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Elizabeth Emma Palmer
- Discipline of Pediatric and Child Health, School of Clinical Medicine, University of New South Wales, Kensington, Australia
- Sydney Children’s Hospitals Network, Sydney, Australia
| | - Toshitaka Yamakawa
- Department of Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, Japan
| | - Makiko Kobayashi
- Department of Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, Japan
| | - Udyavara Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
- School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taizhong, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
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Dogan A, Barua PD, Baygin M, Tuncer T, Dogan S, Yaman O, Dogru AH, Acharya RU. Automated accurate emotion classification using Clefia pattern-based features with EEG signals. International Journal of Healthcare Management 2022. [DOI: 10.1080/20479700.2022.2141694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Abdullah Dogan
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Toowoomba, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Turkey
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Orhan Yaman
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Ali Hikmet Dogru
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Rajendra U. Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Xu S, Faust O, Seoni S, Chakraborty S, Barua PD, Loh HW, Elphick H, Molinari F, Acharya UR. A review of automated sleep disorder detection. Comput Biol Med 2022; 150:106100. [PMID: 36182761 DOI: 10.1016/j.compbiomed.2022.106100] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 09/04/2022] [Accepted: 09/12/2022] [Indexed: 12/22/2022]
Abstract
Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand.
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Affiliation(s)
- Shuting Xu
- Cogninet Brain Team, Sydney, NSW, 2010, Australia
| | - Oliver Faust
- Anglia Ruskin University, East Rd, Cambridge CB1 1PT, UK.
| | - Silvia Seoni
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - Subrata Chakraborty
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW, 2351, Australia; Centre for Advanced Modelling and Geospatial Lnformation Systems (CAMGIS), Faculty of Engineer and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Prabal Datta Barua
- Cogninet Brain Team, Sydney, NSW, 2010, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia; School of Business (Information System), University of Southern Queensland, Australia
| | - Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, 599494, Singapore
| | | | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - U Rajendra Acharya
- School of Business (Information System), University of Southern Queensland, Australia; School of Science and Technology, Singapore University of Social Sciences, 463 Clementi Road, 599494, Singapore; Department of Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.
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Loh HW, Ooi CP, Seoni S, Barua PD, Molinari F, Acharya UR. Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022). Comput Methods Programs Biomed 2022; 226:107161. [PMID: 36228495 DOI: 10.1016/j.cmpb.2022.107161] [Citation(s) in RCA: 79] [Impact Index Per Article: 39.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/16/2022] [Accepted: 09/25/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVES Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community. METHODS Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded. RESULTS In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others. CONCLUSION We discovered that detecting abnormalities in 1D biosignals and identifying key text in clinical notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Silvia Seoni
- Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy
| | - Prabal Datta Barua
- Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia; School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
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Barua PD, Baygin N, Dogan S, Baygin M, Arunkumar N, Fujita H, Tuncer T, Tan RS, Palmer E, Azizan MMB, Kadri NA, Acharya UR. Automated detection of pain levels using deep feature extraction from shutter blinds-based dynamic-sized horizontal patches with facial images. Sci Rep 2022; 12:17297. [PMID: 36241674 PMCID: PMC9568538 DOI: 10.1038/s41598-022-21380-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 09/27/2022] [Indexed: 01/10/2023] Open
Abstract
Pain intensity classification using facial images is a challenging problem in computer vision research. This work proposed a patch and transfer learning-based model to classify various pain intensities using facial images. The input facial images were segmented into dynamic-sized horizontal patches or "shutter blinds". A lightweight deep network DarkNet19 pre-trained on ImageNet1K was used to generate deep features from the shutter blinds and the undivided resized segmented input facial image. The most discriminative features were selected from these deep features using iterative neighborhood component analysis, which were then fed to a standard shallow fine k-nearest neighbor classifier for classification using tenfold cross-validation. The proposed shutter blinds-based model was trained and tested on datasets derived from two public databases-University of Northern British Columbia-McMaster Shoulder Pain Expression Archive Database and Denver Intensity of Spontaneous Facial Action Database-which both comprised four pain intensity classes that had been labeled by human experts using validated facial action coding system methodology. Our shutter blinds-based classification model attained more than 95% overall accuracy rates on both datasets. The excellent performance suggests that the automated pain intensity classification model can be deployed to assist doctors in the non-verbal detection of pain using facial images in various situations (e.g., non-communicative patients or during surgery). This system can facilitate timely detection and management of pain.
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Affiliation(s)
- Prabal Datta Barua
- grid.1048.d0000 0004 0473 0844School of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350 Australia ,grid.117476.20000 0004 1936 7611Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007 Australia
| | - Nursena Baygin
- grid.16487.3c0000 0000 9216 0511Department of Computer Engineering, College of Engineering, Kafkas University, Kars, Turkey
| | - Sengul Dogan
- grid.411320.50000 0004 0574 1529Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Mehmet Baygin
- grid.449062.d0000 0004 0399 2738Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey
| | - N. Arunkumar
- Rathinam College of Engineering, Coimbatore, India
| | - Hamido Fujita
- Faculty of Information Technology, HUTECH University of Technology, Ho Chi Minh City, Viet Nam ,grid.4489.10000000121678994Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, Granada, Spain ,grid.443998.b0000 0001 2172 3919Regional Research Center, Iwate Prefectural University, Iwate, Japan
| | - Turker Tuncer
- grid.411320.50000 0004 0574 1529Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Ru-San Tan
- grid.419385.20000 0004 0620 9905Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore ,grid.428397.30000 0004 0385 0924Duke-NUS Medical School, Singapore, Singapore
| | - Elizabeth Palmer
- grid.430417.50000 0004 0640 6474Centre of Clinical Genetics, Sydney Children’s Hospitals Network, Randwick, 2031 Australia ,grid.1005.40000 0004 4902 0432School of Women’s and Children’s Health, University of New South Wales, Randwick, 2031 Australia
| | - Muhammad Mokhzaini Bin Azizan
- grid.462995.50000 0001 2218 9236Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia (USIM), Nilai, Malaysia
| | - Nahrizul Adib Kadri
- grid.10347.310000 0001 2308 5949Department of Biomedical Engineering, Faculty of Engineering, University Malaya, 50603 Kuala Lumpur, Malaysia
| | - U. Rajendra Acharya
- grid.462630.50000 0000 9158 4937Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489 Singapore ,grid.443365.30000 0004 0388 6484Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Singapore ,grid.252470.60000 0000 9263 9645Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Aydemir E, Baygin M, Dogan S, Tuncer T, Barua PD, Chakraborty S, Faust O, Arunkumar N, Kaysi F, Acharya UR. Mental performance classification using fused multilevel feature generation with EEG signals. International Journal of Healthcare Management 2022. [DOI: 10.1080/20479700.2022.2130645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2022]
Affiliation(s)
- Emrah Aydemir
- Department of Management Information, College of Management, Sakarya University, Sakarya, Turkey
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Darling Heights, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia
| | - Subrata Chakraborty
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, Australia
- Center for Advanced Modelling and Geospatial Information Systems, Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia
| | - Oliver Faust
- School of Computing, Anglia Ruskin University, Cambridge, UK
| | - N. Arunkumar
- Department of Electronics and Instrumentation, SASTRA University, Thanjavur, India
| | - Feyzi Kaysi
- Department of Electronic and Automation, Vocational School of Technical Sciences, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Kaplan E, Altunisik E, Ekmekyapar Firat Y, Datta Barua P, Dogan S, Baygin M, Burak Demir F, Tuncer T, Palmer E, Tan RS, Yu P, Soar J, Fujita H, Rajendra Acharya U. Novel nested patch-based feature extraction model for automated Parkinson's Disease symptom classification using MRI images. Comput Methods Programs Biomed 2022; 224:107030. [PMID: 35878484 DOI: 10.1016/j.cmpb.2022.107030] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/06/2022] [Accepted: 07/15/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE Parkinson's disease (PD) is a common neurological disorder with variable clinical manifestations and magnetic resonance imaging (MRI) findings. We propose a handcrafted image classification model that can accurately (i) classify different PD stages, (ii) detect comorbid dementia, and (iii) discriminate PD-related motor symptoms. METHODS Selected image datasets from three PD studies were used to develop the classification model. Our proposed novel automated system was developed in four phases: (i) texture features are extracted from the non-fixed size patches. In the feature extraction phase, a pyramid histogram-oriented gradient (PHOG) image descriptor is used. (ii) In the feature selection phase, four feature selectors: neighborhood component analysis (NCA), Chi2, minimum redundancy maximum relevancy (mRMR), and ReliefF are used to generate four feature vectors. (iii) Two classifiers: k-nearest neighbor (kNN) and support vector machine (SVM) are used in the classification step. A ten-fold cross-validation technique is used to validate the results. (iv) Eight predicted vectors are generated using four selected feature vectors and two classifiers. Finally, iterative majority voting (IMV) is used to attain general classification results. Therefore, this model is named nested patch-PHOG-multiple feature selectors and multiple classifiers-IMV (NP-PHOG-MFSMCIMV). RESULTS Our presented NP-PHOG-MFSMCIMV model achieved 99.22, 98.70, and 99.53% accuracies for the collected PD stages, PD dementia, and PD symptoms classification datasets, respectively. SIGNIFICANCE The obtained accuracies (over 98% for all states) demonstrated the performance of developed NP-PHOG-MFSMCIMV model in automated PD state classification.
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Affiliation(s)
- Ela Kaplan
- Department of Radiology, Adıyaman Training and Research Hospital, Turkey
| | - Erman Altunisik
- Department of Neurology, Adiyaman University Medicine Faculty, Adiyaman, Turkey
| | | | - Prabal Datta Barua
- School of Business (Information Systems), University of Southern Queensland, Toowoomba, QLD 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey
| | - Fahrettin Burak Demir
- Department of Software Engineering, Faculty of Engineering and Natural Sciences, Bandirma Onyedi Eylul University, Bandirma, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey
| | - Elizabeth Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick 2031, Australia; Discipline of Paediatrics and Child Health, School of Clinical Medicine Randwick, Faculty of Medicine and Health, UNSW, Randwick, NSW 2031, Australia
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore; Duke-NUS Medical School, Singapore
| | - Ping Yu
- School of Computing and Information Technology, University of Wollongong, Wollongong NSW 2522, Australia
| | - Jeffrey Soar
- School of Business (Information Systems), University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Hamido Fujita
- Faculty of Information Technology, HUTECH University of Technology, Ho Chi Minh City, Viet Nam; Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, Granada, Spain; Regional Research Center, Iwate Prefectural University, Iwate, Japan
| | - U Rajendra Acharya
- School of Business (Information Systems), University of Southern Queensland, Toowoomba, QLD 4350, Australia; Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
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Kobat SG, Baygin N, Yusufoglu E, Baygin M, Barua PD, Dogan S, Yaman O, Celiker U, Yildirim H, Tan RS, Tuncer T, Islam N, Acharya UR. Automated Diabetic Retinopathy Detection Using Horizontal and Vertical Patch Division-Based Pre-Trained DenseNET with Digital Fundus Images. Diagnostics (Basel) 2022; 12:diagnostics12081975. [PMID: 36010325 PMCID: PMC9406859 DOI: 10.3390/diagnostics12081975] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 08/08/2022] [Accepted: 08/13/2022] [Indexed: 12/23/2022] Open
Abstract
Diabetic retinopathy (DR) is a common complication of diabetes that can lead to progressive vision loss. Regular surveillance with fundal photography, early diagnosis, and prompt intervention are paramount to reducing the incidence of DR-induced vision loss. However, manual interpretation of fundal photographs is subject to human error. In this study, a new method based on horizontal and vertical patch division was proposed for the automated classification of DR images on fundal photographs. The novel sides of this study are given as follows. We proposed a new non-fixed-size patch division model to obtain high classification results and collected a new fundus image dataset. Moreover, two datasets are used to test the model: a newly collected three-class (normal, non-proliferative DR, and proliferative DR) dataset comprising 2355 DR images and the established open-access five-class Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset comprising 3662 images. Two analysis scenarios, Case 1 and Case 2, with three (normal, non-proliferative DR, and proliferative DR) and five classes (normal, mild DR, moderate DR, severe DR, and proliferative DR), respectively, were derived from the APTOS 2019 dataset. These datasets and these cases have been used to demonstrate the general classification performance of our proposal. By applying transfer learning, the last fully connected and global average pooling layers of the DenseNet201 architecture were used to extract deep features from input DR images and each of the eight subdivided horizontal and vertical patches. The most discriminative features are then selected using neighborhood component analysis. These were fed as input to a standard shallow cubic support vector machine for classification. Our new DR dataset obtained 94.06% and 91.55% accuracy values for three-class classification with 80:20 hold-out validation and 10-fold cross-validation, respectively. As can be seen from steps of the proposed model, a new patch-based deep-feature engineering model has been proposed. The proposed deep-feature engineering model is a cognitive model, since it uses efficient methods in each phase. Similar excellent results were seen for three-class classification with the Case 1 dataset. In addition, the model attained 87.43% and 84.90% five-class classification accuracy rates using 80:20 hold-out validation and 10-fold cross-validation, respectively, on the Case 2 dataset, which outperformed prior DR classification studies based on the five-class APTOS 2019 dataset. Our model attained about >2% classification results compared to others. These findings demonstrate the accuracy and robustness of the proposed model for classification of DR images.
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Affiliation(s)
- Sabiha Gungor Kobat
- Department of Ophthalmology, Firat University Hospital, Firat University, Elazig 23119, Turkey
| | - Nursena Baygin
- Department of Computer Engineering, Faculty of Engineering, Kafkas University, Kars 36000, Turkey
| | - Elif Yusufoglu
- Department of Ophthalmology, Elazig Fethi Sekin City Hospital, Elazig 23280, Turkey
| | - Mehmet Baygin
- Department of Computer Engineering, Faculty of Engineering, Ardahan University, Ardahan 75000, Turkey
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Darling Heights, QLD 4350, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, Turkey
- Correspondence: ; Tel.: +90-424-2370000-7634
| | - Orhan Yaman
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, Turkey
| | - Ulku Celiker
- Department of Ophthalmology, Firat University Hospital, Firat University, Elazig 23119, Turkey
| | - Hakan Yildirim
- Department of Ophthalmology, Firat University Hospital, Firat University, Elazig 23119, Turkey
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore or
- Duke-NUS Medical Centre, Singapore 169857, Singapore
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, Turkey
| | - Nazrul Islam
- Glaucoma Faculty, Bangladesh Eye Hospital & Institute, Dhaka 1209, Bangladesh
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599494, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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Barua PD, Karasu M, Kobat MA, Balık Y, Kivrak T, Baygin M, Dogan S, Demir FB, Tuncer T, Tan RS, Acharya UR. An accurate valvular heart disorders detection model based on a new dual symmetric tree pattern using stethoscope sounds. Comput Biol Med 2022; 146:105599. [DOI: 10.1016/j.compbiomed.2022.105599] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/28/2022] [Accepted: 05/06/2022] [Indexed: 02/02/2023]
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Dogan S, Barua PD, Baygin M, Chakraborty S, Ciaccio E, Tuncer T, Abd Kadir KA, Md Shah MN, Azman RR, Lee CC, Ng KH, Acharya UR. Novel multiple pooling and local phase quantization stable feature extraction techniques for automated classification of brain infarcts. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Faust O, Hong W, Loh HW, Xu S, Tan RS, Chakraborty S, Barua PD, Molinari F, Acharya UR. Heart rate variability for medical decision support systems: A review. Comput Biol Med 2022; 145:105407. [DOI: 10.1016/j.compbiomed.2022.105407] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/09/2022] [Accepted: 03/12/2022] [Indexed: 12/22/2022]
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Tanko D, Barua PD, Dogan S, Tuncer T, Palmer E, Ciaccio EJ, Acharya UR. EPSPatNet86: eight-pointed star pattern learning network for detection ADHD disorder using EEG signals. Physiol Meas 2022; 43. [PMID: 35377344 DOI: 10.1088/1361-6579/ac59dc] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 03/01/2022] [Indexed: 12/22/2022]
Abstract
Objective.The main objective of this work is to present a hand-modelled one-dimensional signal classification system to detect Attention-Deficit Hyperactivity Disorder (ADHD) disorder using electroencephalography (EEG) signals.Approach.A novel handcrafted feature extraction method is presented in this research. Our proposed method uses a directed graph and an eight-pointed star pattern (EPSPat). Also, tunable q wavelet transforms (TQWT), wavelet packet decomposition (WPD), statistical extractor, iterative Chi2 (IChi2) selector, and the k-nearest neighbors (kNN) classifier have been utilized to develop the EPSPat based learning model. This network uses two wavelet decomposition methods (TQWT and WPD), and 85 wavelet coefficient bands are extracted. The proposed EPSPat and statistical feature creator generate features from the 85 wavelet coefficient bands and the original EEG signal. The learning network is termed EPSPatNet86. The main purpose of the presented EPSPatNet86 is to detect abnormalities of the EEG signals. Therefore, 85 wavelet subbands have been generated to extract features. The created 86 feature vectors have been evaluated using the Chi2 selector and the kNN classifier in the loss value calculation phase. The final features vector is created by employing a minimum loss-valued eight feature vectors. The IChi2 selector selects the best feature vector, which is fed to the kNN classifier. An EEG signal dataset has been used to demonstrate the presented model's EEG signal classification ability. We have used an ADHD EEG dataset since ADHD is a commonly seen brain-related ailment.Main results.Our developed EPSPatNet86 model can detect the ADHD EEG signals with 97.19% and 87.60% accuracy using 10-fold cross and subject-wise validations, respectively.Significance.The calculated results demonstrate that the presented EPSPatNet86 attained satisfactory EEG classification ability. Results show that we can apply our developed EPSPatNet86 model to other EEG signal datasets to detect abnormalities.
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Affiliation(s)
- Dahiru Tanko
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Australia.,Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.,Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey
| | - Elizabeth Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick 2031, Australia.,School of Women's and Children's Health, University of New South Wales, Randwick 2031, Australia
| | - Edward J Ciaccio
- Department of Medicine, Columbia University Irving Medical Center, United States of America
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore.,Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore.,Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Loh HW, Ooi CP, Barua PD, Palmer EE, Molinari F, Acharya UR. Automated detection of ADHD: Current trends and future perspective. Comput Biol Med 2022; 146:105525. [DOI: 10.1016/j.compbiomed.2022.105525] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 03/31/2022] [Accepted: 04/04/2022] [Indexed: 12/25/2022]
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Loh HW, Xu S, Faust O, Ooi CP, Barua PD, Chakraborty S, Tan RS, Molinari F, Acharya UR. Application of photoplethysmography signals for healthcare systems: An in-depth review. Comput Methods Programs Biomed 2022; 216:106677. [PMID: 35139459 DOI: 10.1016/j.cmpb.2022.106677] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/30/2022] [Accepted: 01/30/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVES Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals. METHODS We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review. RESULTS Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized. CONCLUSIONS We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Shuting Xu
- Cogninet Australia, Sydney, New South Wales 2010, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
| | - Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, United Kingdom
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Prabal Datta Barua
- Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law and Arts, University of Southern Queensland, Australia
| | - Subrata Chakraborty
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia; Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, 169609, Singapore; Duke-NUS Medical School, 169857, Singapore
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Politecnico di Torino, Italy
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business (Information Systems), Faculty of Business, Education, Law and Arts, University of Southern Queensland, Australia; School of Engineering, Ngee Ann Polytechnic, 535 Clementi Road, 599489, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
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Aydemir E, Dogan S, Baygin M, Ooi CP, Barua PD, Tuncer T, Acharya UR. CGP17Pat: Automated Schizophrenia Detection Based on a Cyclic Group of Prime Order Patterns Using EEG Signals. Healthcare (Basel) 2022; 10:healthcare10040643. [PMID: 35455821 PMCID: PMC9027158 DOI: 10.3390/healthcare10040643] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 03/18/2022] [Accepted: 03/25/2022] [Indexed: 11/16/2022] Open
Abstract
Background and Purpose: Machine learning models have been used to diagnose schizophrenia. The main purpose of this research is to introduce an effective schizophrenia hand-modeled classification method. Method: A public electroencephalogram (EEG) signal data set was used in this work, and an automated schizophrenia detection model is presented using a cyclic group of prime order with a modulo 17 operator. Therefore, the presented feature extractor was named as the cyclic group of prime order pattern, CGP17Pat. Using the proposed CGP17Pat, a new multilevel feature extraction model is presented. To choose a highly distinctive feature, iterative neighborhood component analysis (INCA) was used, and these features were classified using k-nearest neighbors (kNN) with the 10-fold cross-validation and leave-one-subject-out (LOSO) validation techniques. Finally, iterative hard majority voting was employed in the last phase to obtain channel-wise results, and the general results were calculated. Results: The presented CGP17Pat-based EEG classification model attained 99.91% accuracy employing 10-fold cross-validation and 84.33% accuracy using the LOSO strategy. Conclusions: The findings and results depicted the high classification ability of the presented cryptologic pattern for the data set used.
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Affiliation(s)
- Emrah Aydemir
- Department of Management Information Systems, Management Faculty, Sakarya University, Sakarya 54050, Turkey;
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey;
- Correspondence: ; Tel.: +90-424-2370000-7634
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan 75000, Turkey;
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore;
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350, Australia;
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey;
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, Singapore 599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599491, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 413, Taiwan
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Baygin M, Barua PD, Dogan S, Tuncer T, Key S, Acharya UR, Cheong KH. A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal. Sensors (Basel) 2022; 22:2007. [PMID: 35271154 PMCID: PMC8914690 DOI: 10.3390/s22052007] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/11/2022] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
Recently, deep models have been very popular because they achieve excellent performance with many classification problems. Deep networks have high computational complexities and require specific hardware. To overcome this problem (without decreasing classification ability), a hand-modeled feature selection method is proposed in this paper. A new shape-based local feature extractor is presented which uses the geometric shape of the frustum. By using a frustum pattern, textural features are generated. Moreover, statistical features have been extracted in this model. Textures and statistics features are fused, and a hybrid feature extraction phase is obtained; these features are low-level. To generate high level features, tunable Q factor wavelet transform (TQWT) is used. The presented hybrid feature generator creates 154 feature vectors; hence, it is named Frustum154. In the multilevel feature creation phase, this model can select the appropriate feature vectors automatically and create the final feature vector by merging the appropriate feature vectors. Iterative neighborhood component analysis (INCA) chooses the best feature vector, and shallow classifiers are then used. Frustum154 has been tested on three basic hand-movement sEMG datasets. Hand-movement sEMG datasets are commonly used in biomedical engineering, but there are some problems in this area. The presented models generally required one dataset to achieve high classification ability. In this work, three sEMG datasets have been used to test the performance of Frustum154. The presented model is self-organized and selects the most informative subbands and features automatically. It achieved 98.89%, 94.94%, and 95.30% classification accuracies using shallow classifiers, indicating that Frustum154 can improve classification accuracy.
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Affiliation(s)
- Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan 75000, Turkey
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey
| | - Sefa Key
- Department of Orthopedics and Traumatology, Bingöl State Hospital, Ministry of Health, Bingöl 12000, Turkey
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599494, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
| | - Kang Hao Cheong
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore 487372, Singapore
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Baygin M, Yaman O, Barua PD, Dogan S, Tuncer T, Acharya UR. Exemplar Darknet19 feature generation technique for automated kidney stone detection with coronal CT images. Artif Intell Med 2022; 127:102274. [DOI: 10.1016/j.artmed.2022.102274] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 02/07/2022] [Accepted: 03/02/2022] [Indexed: 02/06/2023]
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Aydemir E, Yalcinkaya MA, Barua PD, Baygin M, Faust O, Dogan S, Chakraborty S, Tuncer T, Acharya UR. Hybrid Deep Feature Generation for Appropriate Face Mask Use Detection. Int J Environ Res Public Health 2022; 19:ijerph19041939. [PMID: 35206124 PMCID: PMC8871993 DOI: 10.3390/ijerph19041939] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2022] [Revised: 01/29/2022] [Accepted: 01/30/2022] [Indexed: 12/04/2022]
Abstract
Mask usage is one of the most important precautions to limit the spread of COVID-19. Therefore, hygiene rules enforce the correct use of face coverings. Automated mask usage classification might be used to improve compliance monitoring. This study deals with the problem of inappropriate mask use. To address that problem, 2075 face mask usage images were collected. The individual images were labeled as either mask, no masked, or improper mask. Based on these labels, the following three cases were created: Case 1: mask versus no mask versus improper mask, Case 2: mask versus no mask + improper mask, and Case 3: mask versus no mask. This data was used to train and test a hybrid deep feature-based masked face classification model. The presented method comprises of three primary stages: (i) pre-trained ResNet101 and DenseNet201 were used as feature generators; each of these generators extracted 1000 features from an image; (ii) the most discriminative features were selected using an improved RelieF selector; and (iii) the chosen features were used to train and test a support vector machine classifier. That resulting model attained 95.95%, 97.49%, and 100.0% classification accuracy rates on Case 1, Case 2, and Case 3, respectively. Having achieved these high accuracy values indicates that the proposed model is fit for a practical trial to detect appropriate face mask use in real time.
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Affiliation(s)
- Emrah Aydemir
- Department of Management Information, College of Management, Sakarya University, Sakarya 54050, Turkey;
| | - Mehmet Ali Yalcinkaya
- Department of Computer Engineering, Engineering Faculty, Kirsehir Ahi Evran University, Kirsehir 40100, Turkey;
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia;
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Mehmet Baygin
- Department of Computer Engineering, Faculty of Engineering, Ardahan University, Ardahan 75000, Turkey;
| | - Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK
- Correspondence:
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey; (S.D.); (T.T.)
| | - Subrata Chakraborty
- School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia;
- Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey; (S.D.); (T.T.)
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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Bargshady G, Zhou X, Barua PD, Gururajan R, Li Y, Acharya UR. Application of CycleGAN and transfer learning techniques for automated detection of COVID-19 using X-ray images. Pattern Recognit Lett 2022; 153:67-74. [PMID: 34876763 PMCID: PMC8641403 DOI: 10.1016/j.patrec.2021.11.020] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 10/15/2021] [Accepted: 11/18/2021] [Indexed: 12/02/2022]
Abstract
Coronavirus (which is also known as COVID-19) is severely impacting the wellness and lives of many across the globe. There are several methods currently to detect and monitor the progress of the disease such as radiological image from patients' chests, measuring the symptoms and applying polymerase chain reaction (RT-PCR) test. X-ray imaging is one of the popular techniques used to visualise the impact of the virus on the lungs. Although manual detection of this disease using radiology images is more popular, it can be time-consuming, and is prone to human errors. Hence, automated detection of lung pathologies due to COVID-19 utilising deep learning (Bowles et al.) techniques can assist with yielding accurate results for huge databases. Large volumes of data are needed to achieve generalizable DL models; however, there are very few public databases available for detecting COVID-19 disease pathologies automatically. Standard data augmentation method can be used to enhance the models' generalizability. In this research, the Extensive COVID-19 X-ray and CT Chest Images Dataset has been used and generative adversarial network (GAN) coupled with trained, semi-supervised CycleGAN (SSA- CycleGAN) has been applied to augment the training dataset. Then a newly designed and finetuned Inception V3 transfer learning model has been developed to train the algorithm for detecting COVID-19 pandemic. The obtained results from the proposed Inception-CycleGAN model indicated Accuracy = 94.2%, Area under Curve = 92.2%, Mean Squared Error = 0.27, Mean Absolute Error = 0.16. The developed Inception-CycleGAN framework is ready to be tested with further COVID-19 X-Ray images of the chest.
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Affiliation(s)
- Ghazal Bargshady
- School of Business, University of Southern Queensland, 37 Sinnathamby Blvd, Springfield Central, QLD 4300, Australia
| | - Xujuan Zhou
- School of Business, University of Southern Queensland, 37 Sinnathamby Blvd, Springfield Central, QLD 4300, Australia
| | - Prabal Datta Barua
- School of Business, University of Southern Queensland, 37 Sinnathamby Blvd, Springfield Central, QLD 4300, Australia
| | - Raj Gururajan
- School of Business, University of Southern Queensland, 37 Sinnathamby Blvd, Springfield Central, QLD 4300, Australia
| | - Yuefeng Li
- School of Computer Science, Queensland University of Technology, Australia
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore
- Department Bioinformatics and Medical Engineering, Asia University, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, Japan
- School of Science and Technology, Singapore University of Social Sciences, Singapore
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Barua PD, Chan WY, Dogan S, Baygin M, Tuncer T, Ciaccio EJ, Islam N, Cheong KH, Shahid ZS, Acharya UR. Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images. Entropy (Basel) 2021; 23:1651. [PMID: 34945957 PMCID: PMC8700736 DOI: 10.3390/e23121651] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/22/2021] [Accepted: 11/25/2021] [Indexed: 01/04/2023]
Abstract
Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.
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Affiliation(s)
- Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia;
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Wai Yee Chan
- University Malaya Research Imaging Centre, Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 59100, Malaysia;
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23002, Turkey; (S.D.); (T.T.)
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan 75000, Turkey;
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23002, Turkey; (S.D.); (T.T.)
| | - Edward J. Ciaccio
- Department of Medicine, Columbia University Irving Medical Center, New York, NY 10032-3784, USA;
| | - Nazrul Islam
- Glaucoma Faculty, Bangladesh Eye Hospital & Institute, Dhaka 1206, Bangladesh;
| | - Kang Hao Cheong
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore 487372, Singapore
| | - Zakia Sultana Shahid
- Department of Ophthalmology, Anwer Khan Modern Medical College, Dhaka 1205, Bangladesh;
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 129799, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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Gudigar A, Raghavendra U, Nayak S, Ooi CP, Chan WY, Gangavarapu MR, Dharmik C, Samanth J, Kadri NA, Hasikin K, Barua PD, Chakraborty S, Ciaccio EJ, Acharya UR. Role of Artificial Intelligence in COVID-19 Detection. Sensors (Basel) 2021; 21:8045. [PMID: 34884045 PMCID: PMC8659534 DOI: 10.3390/s21238045] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 11/26/2021] [Accepted: 11/26/2021] [Indexed: 12/15/2022]
Abstract
The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.
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Affiliation(s)
- Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Sneha Nayak
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore;
| | - Wai Yee Chan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia;
| | - Mokshagna Rohit Gangavarapu
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Chinmay Dharmik
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Jyothi Samanth
- Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Nahrizul Adib Kadri
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.A.K.); (K.H.)
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.A.K.); (K.H.)
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia;
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Subrata Chakraborty
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
- Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
| | - Edward J. Ciaccio
- Department of Medicine, Columbia University Medical Center, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore;
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
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Loh HW, Hong W, Ooi CP, Chakraborty S, Barua PD, Deo RC, Soar J, Palmer EE, Acharya UR. Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021). Sensors (Basel) 2021; 21:s21217034. [PMID: 34770340 PMCID: PMC8587636 DOI: 10.3390/s21217034] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/07/2021] [Accepted: 10/19/2021] [Indexed: 12/18/2022]
Abstract
Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
| | - Wanrong Hong
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
| | - Subrata Chakraborty
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Ravinesh C Deo
- School of Sciences, University of Southern Queensland, Springfield, QLD 4300, Australia
| | - Jeffrey Soar
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Elizabeth E Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick, NSW 2031, Australia
- School of Women's and Children's Health, University of New South Wales, Randwick, NSW 2031, Australia
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 413, Taiwan
- Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
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Kobat MA, Kivrak T, Barua PD, Tuncer T, Dogan S, Tan RS, Ciaccio EJ, Acharya UR. Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds. Diagnostics (Basel) 2021; 11:1962. [PMID: 34829308 PMCID: PMC8620352 DOI: 10.3390/diagnostics11111962] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 10/17/2021] [Accepted: 10/19/2021] [Indexed: 01/22/2023] Open
Abstract
COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds.
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Affiliation(s)
- Mehmet Ali Kobat
- Department of Cardiology, Firat University Hospital, Firat University, Elazig 23119, Turkey; (M.A.K.); (T.K.)
| | - Tarik Kivrak
- Department of Cardiology, Firat University Hospital, Firat University, Elazig 23119, Turkey; (M.A.K.); (T.K.)
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia;
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey; (T.T.); (S.D.)
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey; (T.T.); (S.D.)
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore;
- Department of Cardiology, Duke-NUS Graduate Medical School, Singapore 169857, Singapore
| | - Edward J. Ciaccio
- Department of Medicine, Celiac Disease Center, Columbia University Irving Medical Center, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Clementi 599494, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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Mayya V, Kamath Shevgoor S, Kulkarni U, Hazarika M, Barua PD, Acharya UR. Multi-Scale Convolutional Neural Network for Accurate Corneal Segmentation in Early Detection of Fungal Keratitis. J Fungi (Basel) 2021; 7:jof7100850. [PMID: 34682271 PMCID: PMC8540278 DOI: 10.3390/jof7100850] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/04/2021] [Accepted: 10/06/2021] [Indexed: 11/16/2022] Open
Abstract
Microbial keratitis is an infection of the cornea of the eye that is commonly caused by prolonged contact lens wear, corneal trauma, pre-existing systemic disorders and other ocular surface disorders. It can result in severe visual impairment if improperly managed. According to the latest World Vision Report, at least 4.2 million people worldwide suffer from corneal opacities caused by infectious agents such as fungi, bacteria, protozoa and viruses. In patients with fungal keratitis (FK), often overt symptoms are not evident, until an advanced stage. Furthermore, it has been reported that clear discrimination between bacterial keratitis and FK is a challenging process even for trained corneal experts and is often misdiagnosed in more than 30% of the cases. However, if diagnosed early, vision impairment can be prevented through early cost-effective interventions. In this work, we propose a multi-scale convolutional neural network (MS-CNN) for accurate segmentation of the corneal region to enable early FK diagnosis. The proposed approach consists of a deep neural pipeline for corneal region segmentation followed by a ResNeXt model to differentiate between FK and non-FK classes. The model trained on the segmented images in the region of interest, achieved a diagnostic accuracy of 88.96%. The features learnt by the model emphasize that it can correctly identify dominant corneal lesions for detecting FK.
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Affiliation(s)
- Veena Mayya
- Healthcare Analytics and Language Engineering (HALE) Lab, Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore 575025, India;
- Department of Information & Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal 576104, India
- Correspondence: (V.M.); (M.H.)
| | - Sowmya Kamath Shevgoor
- Healthcare Analytics and Language Engineering (HALE) Lab, Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore 575025, India;
| | - Uma Kulkarni
- Department of Ophthalmology, Yenepoya Medical College, Yenepoya (Deemed to Be University), Mangalore 575018, India;
| | - Manali Hazarika
- Cornea and Anterior Segment Services, Department of Ophthalmology, Kasturba Medical College, Manipal Academy of Higher Education (MAHE), Manipal 576104, India
- Correspondence: (V.M.); (M.H.)
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia;
- Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore S599494, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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Baygin M, Yaman O, Tuncer T, Dogan S, Barua PD, Acharya UR. Automated accurate schizophrenia detection system using Collatz pattern technique with EEG signals. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102936] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Barua PD, Muhammad Gowdh NF, Rahmat K, Ramli N, Ng WL, Chan WY, Kuluozturk M, Dogan S, Baygin M, Yaman O, Tuncer T, Wen T, Cheong KH, Acharya UR. Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images. Int J Environ Res Public Health 2021; 18:8052. [PMID: 34360343 PMCID: PMC8345793 DOI: 10.3390/ijerph18158052] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/21/2021] [Accepted: 07/22/2021] [Indexed: 12/18/2022]
Abstract
COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application.
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Affiliation(s)
- Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba 2550, Australia;
| | - Nadia Fareeda Muhammad Gowdh
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.F.M.G.); (K.R.); (N.R.); (W.L.N.); (W.Y.C.)
| | - Kartini Rahmat
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.F.M.G.); (K.R.); (N.R.); (W.L.N.); (W.Y.C.)
| | - Norlisah Ramli
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.F.M.G.); (K.R.); (N.R.); (W.L.N.); (W.Y.C.)
| | - Wei Lin Ng
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.F.M.G.); (K.R.); (N.R.); (W.L.N.); (W.Y.C.)
| | - Wai Yee Chan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.F.M.G.); (K.R.); (N.R.); (W.L.N.); (W.Y.C.)
| | - Mutlu Kuluozturk
- Department of Pulmonology Clinic, Firat University Hospital, Firat University, Elazig 23119, Turkey;
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey; (O.Y.); (T.T.)
| | - Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan 75000, Turkey;
| | - Orhan Yaman
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey; (O.Y.); (T.T.)
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey; (O.Y.); (T.T.)
| | - Tao Wen
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, 8 Somapah Road, Singapore S485998, Singapore;
| | - Kang Hao Cheong
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, 8 Somapah Road, Singapore S485998, Singapore;
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore S599489, Singapore;
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore S599494, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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Baygin M, Dogan S, Tuncer T, Datta Barua P, Faust O, Arunkumar N, Abdulhay EW, Emma Palmer E, Rajendra Acharya U. Automated ASD detection using hybrid deep lightweight features extracted from EEG signals. Comput Biol Med 2021; 134:104548. [PMID: 34119923 DOI: 10.1016/j.compbiomed.2021.104548] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 05/28/2021] [Accepted: 05/31/2021] [Indexed: 01/14/2023]
Abstract
BACKGROUND Autism spectrum disorder is a common group of conditions affecting about one in 54 children. Electroencephalogram (EEG) signals from children with autism have a common morphological pattern which makes them distinguishable from normal EEG. We have used this type of signal to design and implement an automated autism detection model. MATERIALS AND METHOD We propose a hybrid lightweight deep feature extractor to obtain high classification performance. The system was designed and tested with a big EEG dataset that contained signals from autism patients and normal controls. (i) A new signal to image conversion model is presented in this paper. In this work, features are extracted from EEG signal using one-dimensional local binary pattern (1D_LBP) and the generated features are utilized as input of the short time Fourier transform (STFT) to generate spectrogram images. (ii) The deep features of the generated spectrogram images are extracted using a combination of pre-trained MobileNetV2, ShuffleNet, and SqueezeNet models. This method is named hybrid deep lightweight feature generator. (iii) A two-layered ReliefF algorithm is used for feature ranking and feature selection. (iv) The most discriminative features are fed to various shallow classifiers, developed using a 10-fold cross-validation strategy for automated autism detection. RESULTS A support vector machine (SVM) classifier reached 96.44% accuracy based on features from the proposed model. CONCLUSIONS The results strongly indicate that the proposed hybrid deep lightweight feature extractor is suitable for autism detection using EEG signals. The model is ready to serve as part of an adjunct tool that aids neurologists during autism diagnosis in medical centers.
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Affiliation(s)
- Mehmet Baygin
- Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey.
| | - Sengul Dogan
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey.
| | - Turker Tuncer
- Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey.
| | - Prabal Datta Barua
- School of Management & Enterprise, University of Southern Queensland, Australia.
| | - Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield, United Kingdom.
| | - N Arunkumar
- Department of Electronics and Instrumentation, SASTRA University, Thirumalaisamudram, Thanjavur, 613401, India.
| | - Enas W Abdulhay
- Biomedical Engineering Department, Faculty of Engineering, Jordan University of Science and Technology, P.O.Box 3030, Irbid, 22110, Jordan.
| | - Elizabeth Emma Palmer
- Department of Medical Genetics, Sydney Children's Hospital, High Street, Randwick, NSW, Australia.
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan.
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