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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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Visuña L, Yang D, Garcia-Blas J, Carretero J. Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning. BMC Med Imaging 2022; 22:178. [PMID: 36243705 PMCID: PMC9568999 DOI: 10.1186/s12880-022-00904-4] [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: 05/10/2022] [Accepted: 09/05/2022] [Indexed: 11/16/2022] Open
Abstract
Background Nowadays doctors and radiologists are overwhelmed with a huge amount of work. This led to the effort to design different Computer-Aided Diagnosis systems (CAD system), with the aim of accomplishing a faster and more accurate diagnosis. The current development of deep learning is a big opportunity for the development of new CADs. In this paper, we propose a novel architecture for a convolutional neural network (CNN) ensemble for classifying chest X-ray (CRX) images into four classes: viral Pneumonia, Tuberculosis, COVID-19, and Healthy. Although Computed tomography (CT) is the best way to detect and diagnoses pulmonary issues, CT is more expensive than CRX. Furthermore, CRX is commonly the first step in the diagnosis, so it’s very important to be accurate in the early stages of diagnosis and treatment. Results We applied the transfer learning technique and data augmentation to all CNNs for obtaining better performance. We have designed and evaluated two different CNN-ensembles: Stacking and Voting. This system is ready to be applied in a CAD system to automated diagnosis such a second or previous opinion before the doctors or radiology’s. Our results show a great improvement, 99% accuracy of the Stacking Ensemble and 98% of accuracy for the the Voting Ensemble. Conclusions To minimize missclassifications, we included six different base CNN models in our architecture (VGG16, VGG19, InceptionV3, ResNet101V2, DenseNet121 and CheXnet) and it could be extended to any number as well as we expect extend the number of diseases to detected. The proposed method has been validated using a large dataset created by mixing several public datasets with different image sizes and quality. As we demonstrate in the evaluation carried out, we reach better results and generalization compared with previous works. In addition, we make a first approach to explainable deep learning with the objective of providing professionals more information that may be valuable when evaluating CRXs.
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Affiliation(s)
- Lara Visuña
- Department of Computer Science and Engineering, University Carlos III, Madrid, Spain
| | - Dandi Yang
- Beijing Electro-Mechanical Engineering Institute, Beijing, China
| | - Javier Garcia-Blas
- Department of Computer Science and Engineering, University Carlos III, Madrid, Spain
| | - Jesus Carretero
- Department of Computer Science and Engineering, University Carlos III, Madrid, Spain.
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Jothimani D, Venugopal R, Manoharan S, Danielraj S, Palanichamy S, Narasimhan G, Kaliamoorthy I, Rela M. COVID-19: Time for a clinical classification? INDIAN J PATHOL MICR 2022; 65:902-906. [PMID: 36308203 DOI: 10.4103/ijpm.ijpm_43_21] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023] Open
Abstract
COVID-19 pandemic caused by SARS-CoV-2 virus has been around for 2 years causing significant health-care catastrophes in most parts of the world. The understanding of COVID-19 continues to expand, with multiple newer developments such as the presence of asymptomatic cases, feco-oral transmission, and endothelial dysfunction. The existing classification was developed before this current understanding. With the availability of recent literature evidences, we have attempted a classification encompassing pathogenesis and clinical features for better understanding of the disease process. The pathogenesis of COVID-19 continues to evolve. The spiked protein of the SARS-CoV-2 virus binds to ACE2 receptors causes direct cytopathic damage and hyperinflammatory injury. In addition to alveolar cells, ACE2 is also distributed in gastrointestinal tract and vascular endothelium. ACE2-SARS-CoV-2 interaction engulfs the receptors leading to depletion. Accumulation of Ang2 via AT1 receptor (AT1R) binding causes upregulation of macrophage activity leading to pro-inflammatory cytokine release. Interleukin-6 (IL-6) has been attributed to cause hyperinflammatory syndrome in COVID-19. In addition, it also causes severe widespread endothelial injury through soluble IL-6 receptors. Thrombotic complications occur following the cleavage and activation of von Willebrand factor. Based on the above understanding, clinical features, organ involvement, risk stratification, and disease severity, we have classified COVID-19 patients into asymptomatic, pulmonary, GI, and systemic COVID-19 (S-COVID-19). Studies show that the infectivity and prognosis are different and distinct amongst these groups. Systemic-COVID-19 patients are more likely to be critically ill with multi-organ dysfunction and thrombo-embolic complications.
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Affiliation(s)
- Dinesh Jothimani
- Institute of Liver disease and Transplantation, Dr. Rela Institute and Medical Centre, Bharat Institute of Higher Education and Research, Chennai, Tamil Nadu, India
| | - Radhika Venugopal
- Institute of Liver disease and Transplantation, Dr. Rela Institute and Medical Centre, Bharat Institute of Higher Education and Research, Chennai, Tamil Nadu, India
| | - Shruthi Manoharan
- Institute of Liver disease and Transplantation, Dr. Rela Institute and Medical Centre, Bharat Institute of Higher Education and Research, Chennai, Tamil Nadu, India
| | - Silas Danielraj
- Institute of Liver disease and Transplantation, Dr. Rela Institute and Medical Centre, Bharat Institute of Higher Education and Research, Chennai, Tamil Nadu, India
| | - Swetha Palanichamy
- Institute of Liver disease and Transplantation, Dr. Rela Institute and Medical Centre, Bharat Institute of Higher Education and Research, Chennai, Tamil Nadu, India
| | - Gomathy Narasimhan
- Institute of Liver disease and Transplantation, Dr. Rela Institute and Medical Centre, Bharat Institute of Higher Education and Research, Chennai, Tamil Nadu, India
| | - Ilankumaran Kaliamoorthy
- Institute of Liver disease and Transplantation, Dr. Rela Institute and Medical Centre, Bharat Institute of Higher Education and Research, Chennai, Tamil Nadu, India
| | - Mohamed Rela
- Institute of Liver disease and Transplantation, Dr. Rela Institute and Medical Centre, Bharat Institute of Higher Education and Research, Chennai, Tamil Nadu, India
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Akbarimajd A, Hoertel N, Hussain MA, Neshat AA, Marhamati M, Bakhtoor M, Momeny M. Learning-to-augment incorporated noise-robust deep CNN for detection of COVID-19 in noisy X-ray images. J Comput Sci 2022; 63:101763. [PMID: 35818367 PMCID: PMC9259198 DOI: 10.1016/j.jocs.2022.101763] [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] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 05/16/2022] [Accepted: 06/21/2022] [Indexed: 05/05/2023]
Abstract
Deep convolutional neural networks (CNNs) are used for the detection of COVID-19 in X-ray images. The detection performance of deep CNNs may be reduced by noisy X-ray images. To improve the robustness of a deep CNN against impulse noise, we propose a novel CNN approach using adaptive convolution, with the aim to ameliorate COVID-19 detection in noisy X-ray images without requiring any preprocessing for noise removal. This approach includes an impulse noise-map layer, an adaptive resizing layer, and an adaptive convolution layer to the conventional CNN framework. We also used a learning-to-augment strategy using noisy X-ray images to improve the generalization of a deep CNN. We have collected a dataset of 2093 chest X-ray images including COVID-19 (452 images), non-COVID pneumonia (621 images), and healthy ones (1020 images). The architecture of pre-trained networks such as SqueezeNet, GoogleNet, MobileNetv2, ResNet18, ResNet50, ShuffleNet, and EfficientNetb0 has been modified to increase their robustness to impulse noise. Validation on the noisy X-ray images using the proposed noise-robust layers and learning-to-augment strategy-incorporated ResNet50 showed 2% better classification accuracy compared with state-of-the-art method.
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Affiliation(s)
- Adel Akbarimajd
- Department of Electrical and Computer Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
| | - Nicolas Hoertel
- AP-HP.Centre, Département Médico-Universitaire de Psychiatrie et Addictologie, Hôpital Corentin-Celton, 92130 Issy-les-Moulineaux, France
- Université de Paris, Paris, France
- INSERM, Institut de Psychiatrie et Neurosciences de Paris, UMR_S1266, Paris, France
| | | | | | | | - Mahdi Bakhtoor
- Department of Computer Science, Shirvan Branch, Islamic Azad University, Shirvan, Iran
| | - Mohammad Momeny
- Department of Electrical and Computer Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
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Hassan H, Ren Z, Zhao H, Huang S, Li D, Xiang S, Kang Y, Chen S, Huang B. Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks. Comput Biol Med 2022; 141:105123. [PMID: 34953356 PMCID: PMC8684223 DOI: 10.1016/j.compbiomed.2021.105123] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [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: 09/29/2021] [Revised: 12/03/2021] [Accepted: 12/03/2021] [Indexed: 01/12/2023]
Abstract
This article presents a systematic overview of artificial intelligence (AI) and computer vision strategies for diagnosing the coronavirus disease of 2019 (COVID-19) using computerized tomography (CT) medical images. We analyzed the previous review works and found that all of them ignored classifying and categorizing COVID-19 literature based on computer vision tasks, such as classification, segmentation, and detection. Most of the COVID-19 CT diagnosis methods comprehensively use segmentation and classification tasks. Moreover, most of the review articles are diverse and cover CT as well as X-ray images. Therefore, we focused on the COVID-19 diagnostic methods based on CT images. Well-known search engines and databases such as Google, Google Scholar, Kaggle, Baidu, IEEE Xplore, Web of Science, PubMed, ScienceDirect, and Scopus were utilized to collect relevant studies. After deep analysis, we collected 114 studies and reported highly enriched information for each selected research. According to our analysis, AI and computer vision have substantial potential for rapid COVID-19 diagnosis as they could significantly assist in automating the diagnosis process. Accurate and efficient models will have real-time clinical implications, though further research is still required. Categorization of literature based on computer vision tasks could be helpful for future research; therefore, this review article will provide a good foundation for conducting such research.
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Affiliation(s)
- Haseeb Hassan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China; Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China
| | - Zhaoyu Ren
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Huishi Zhao
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Shoujin Huang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Dan Li
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Shaohua Xiang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Yan Kang
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, Shenzhen, China; Medical Device Innovation Research Center, Shenzhen Technology University, Shenzhen, China
| | - Sifan Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China; Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Bingding Huang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China.
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Abstract
COVID-19 is an acronym for coronavirus disease 2019. Initially, it was called 2019-nCoV, and later International Committee on Taxonomy of Viruses (ICTV) termed it SARS-CoV-2. On 30th January 2020, the World Health Organization (WHO) declared it a pandemic. With an increasing number of COVID-19 cases, the available medical infrastructure is essential to detect the suspected cases. Medical imaging techniques such as Computed Tomography (CT), chest radiography can play an important role in the early screening and detection of COVID-19 cases. It is important to identify and separate the cases to stop the further spread of the virus. Artificial Intelligence can play an important role in COVID-19 detection and decreases the workload on collapsing medical infrastructure. In this paper, a deep convolutional neural network-based architecture is proposed for the COVID-19 detection using chest radiographs. The dataset used to train and test the model is available on different public repositories. Despite having the high accuracy of the model, the decision on COVID-19 should be made in consultation with the trained medical clinician.
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Affiliation(s)
- Tarun Agrawal
- Department of Computer Science and Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh 177005 India
| | - Prakash Choudhary
- Department of Computer Science and Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh 177005 India
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