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Fan Y, Liu M, Sun G. An interpretable machine learning framework for diagnosis and prognosis of COVID-19. PLoS One 2023; 18:e0291961. [PMID: 37733828 PMCID: PMC10513274 DOI: 10.1371/journal.pone.0291961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 09/10/2023] [Indexed: 09/23/2023] Open
Abstract
Coronaviruses have affected the lives of people around the world. Increasingly, studies have indicated that the virus is mutating and becoming more contagious. Hence, the pressing priority is to swiftly and accurately predict patient outcomes. In addition, physicians and patients increasingly need interpretability when building machine models in healthcare. We propose an interpretable machine framework(KISM) that can diagnose and prognose patients based on blood test datasets. First, we use k-nearest neighbors, isolated forests, and SMOTE to pre-process the original blood test datasets. Seven machine learning tools Support Vector Machine, Extra Tree, Random Forest, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, Logistic Regression, and ensemble learning were then used to diagnose and predict COVID-19. In addition, we used SHAP and scikit-learn post-hoc interpretability to report feature importance, allowing healthcare professionals and artificial intelligence models to interact to suggest biomarkers that some doctors may have missed. The 10-fold cross-validation of two public datasets shows that the performance of KISM is better than that of the current state-of-the-art methods. In the diagnostic COVID-19 task, an AUC value of 0.9869 and an accuracy of 0.9787 were obtained, and ultimately Leukocytes, platelets, and Proteina C reativa mg/dL were found to be the most indicative biomarkers for the diagnosis of COVID-19. An AUC value of 0.9949 and an accuracy of 0.9677 were obtained in the prognostic COVID-19 task and Age, LYMPH, and WBC were found to be the most indicative biomarkers for identifying the severity of the patient.
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Affiliation(s)
- Yongxian Fan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Meng Liu
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Guicong Sun
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China
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Sharma R, Mahanti GK, Panda G, Rath A, Dash S, Mallik S, Hu R. A Framework for Detecting Thyroid Cancer from Ultrasound and Histopathological Images Using Deep Learning, Meta-Heuristics, and MCDM Algorithms. J Imaging 2023; 9:173. [PMID: 37754937 PMCID: PMC10532397 DOI: 10.3390/jimaging9090173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/17/2023] [Accepted: 08/22/2023] [Indexed: 09/28/2023] Open
Abstract
Computer-assisted diagnostic systems have been developed to aid doctors in diagnosing thyroid-related abnormalities. The aim of this research is to improve the diagnosis accuracy of thyroid abnormality detection models that can be utilized to alleviate undue pressure on healthcare professionals. In this research, we proposed deep learning, metaheuristics, and a MCDM algorithms-based framework to detect thyroid-related abnormalities from ultrasound and histopathological images. The proposed method uses three recently developed deep learning techniques (DeiT, Swin Transformer, and Mixer-MLP) to extract features from the thyroid image datasets. The feature extraction techniques are based on the Image Transformer and MLP models. There is a large number of redundant features that can overfit the classifiers and reduce the generalization capabilities of the classifiers. In order to avoid the overfitting problem, six feature transformation techniques (PCA, TSVD, FastICA, ISOMAP, LLE, and UMP) are analyzed to reduce the dimensionality of the data. There are five different classifiers (LR, NB, SVC, KNN, and RF) evaluated using the 5-fold stratified cross-validation technique on the transformed dataset. Both datasets exhibit large class imbalances and hence, the stratified cross-validation technique is used to evaluate the performance. The MEREC-TOPSIS MCDM technique is used for ranking the evaluated models at different analysis stages. In the first stage, the best feature extraction and classification techniques are chosen, whereas, in the second stage, the best dimensionality reduction method is evaluated in wrapper feature selection mode. Two best-ranked models are further selected for the weighted average ensemble learning and features selection using the recently proposed meta-heuristics FOX-optimization algorithm. The PCA+FOX optimization-based feature selection + random forest model achieved the highest TOPSIS score and performed exceptionally well with an accuracy of 99.13%, F2-score of 98.82%, and AUC-ROC score of 99.13% on the ultrasound dataset. Similarly, the model achieved an accuracy score of 90.65%, an F2-score of 92.01%, and an AUC-ROC score of 95.48% on the histopathological dataset. This study exploits the combination novelty of different algorithms in order to improve the thyroid cancer diagnosis capabilities. This proposed framework outperforms the current state-of-the-art diagnostic methods for thyroid-related abnormalities in ultrasound and histopathological datasets and can significantly aid medical professionals by reducing the excessive burden on the medical fraternity.
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Affiliation(s)
- Rohit Sharma
- Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur 713209, India; (R.S.); (G.K.M.)
| | - Gautam Kumar Mahanti
- Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur 713209, India; (R.S.); (G.K.M.)
| | - Ganapati Panda
- Department of Electronics and Communication Engineering, C.V. Raman Global University, Bhubaneswar 752054, India;
| | - Adyasha Rath
- Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar 752054, India;
| | - Sujata Dash
- Department of Information Technology, Nagaland University, Dimapur 797112, India;
| | - Saurav Mallik
- Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA 02115, USA
- Department of Pharmacology & Toxicology, The University of Arizona, Tucson, MA 85721, USA
| | - Ruifeng Hu
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
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Cai C, Gou B, Khishe M, Mohammadi M, Rashidi S, Moradpour R, Mirjalili S. Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images. EXPERT SYSTEMS WITH APPLICATIONS 2023; 213:119206. [PMID: 36348736 PMCID: PMC9633109 DOI: 10.1016/j.eswa.2022.119206] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/17/2022] [Accepted: 10/31/2022] [Indexed: 05/11/2023]
Abstract
Applying Deep Learning (DL) in radiological images (i.e., chest X-rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers' trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the Chimp Optimization Algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. In addition, two publicly accessible datasets termed COVID-Xray-5 k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-Chimp. In order to make a fair comparison, two structures are proposed: i-6c-2 s-12c-2 s and i-8c-2 s-16c-2 s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic Algorithm (DCNN-GA), and Matched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarm rate of less than 0.89%. The outcomes show that the DCCN-Chimp can deliver noticeably superior results than the comparable detectors. The Class Activation Map (CAM) is another tool used in this study to identify probable COVID-19-infected areas. Results show that highlighted regions are completely connected with clinical outcomes, which has been verified by experts.
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Affiliation(s)
- Chengfeng Cai
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Bingchen Gou
- School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Mohammad Khishe
- Departement of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
| | - Mokhtar Mohammadi
- Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Kurdistan Region, Iraq
| | - Shima Rashidi
- Department of Computer Science, College of Science and Technology, University of Human Development, Sulaymaniyah, Kurdistan Region, Iraq
| | - Reza Moradpour
- Departement of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran
| | - Seyedali Mirjalili
- Centre for Artificial Intelligence Research and Optimization, Torrens University, Australia
- University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary
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Alamoodi AH, Zaidan BB, Albahri OS, Garfan S, Ahmaro IYY, Mohammed RT, Zaidan AA, Ismail AR, Albahri AS, Momani F, Al-Samarraay MS, Jasim AN, R.Q.Malik. Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions. COMPLEX INTELL SYST 2023; 9:1-27. [PMID: 36777815 PMCID: PMC9895977 DOI: 10.1007/s40747-023-00972-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 01/01/2023] [Indexed: 02/05/2023]
Abstract
When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic's main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis (n = 6), (2) safety (n = 11), (3) hospital (n = 8), (4) treatment (n = 4), and (5) review (n = 3). A bibliographic analysis is also presented on the basis of annual scientific production, country scientific production, co-occurrence, and co-authorship. A comprehensive discussion is also presented to discuss the main challenges, motivations, and recommendations in using MCDM research in COVID-19-related medial case studies. Lastly, we identify critical research gaps with their corresponding solutions and detailed methodologies to serve as a guide for future directions. In conclusion, MCDM can be utilized in the medical field effectively to optimize the resources and make the best choices particularly during pandemics and natural disasters.
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Affiliation(s)
- A. H. Alamoodi
- Faculty of Computing and Meta-Technology (FKMT), Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia
| | - B. B. Zaidan
- Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliu, Yunlin 64002 Taiwan, ROC
| | - O. S. Albahri
- Computer Techniques Engineering Department, Mazaya University College, Nasiriyah, Iraq
| | - Salem Garfan
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia
| | - Ibraheem Y. Y. Ahmaro
- Computer Science Department, College of Information Technology, Hebron University, Hebron, Palestine
| | - R. T. Mohammed
- Department of Computing Science, Komar University of Science and Technology (KUST), Sulaymaniyah, Iraq
| | - A. A. Zaidan
- SP Jain School of Global Management, Sydney, Australia
| | - Amelia Ritahani Ismail
- Department of Computer Science, Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur, Malaysia
| | - A. S. Albahri
- Iraqi Commission for Computers and Informatics (ICCI), Baghdad, Iraq
| | - Fayiz Momani
- E-Business and Commerce Department, Faculty of Administrative and Financial Sciences, University of Petra, Amman, 961343 Jordan
| | - Mohammed S. Al-Samarraay
- Department of Computing, Faculty of Arts, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia
| | | | - R.Q.Malik
- Medical Intrumentation Techniques Engineering Department, Al-Mustaqbal University College, Babylon, Iraq
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Al-Waisy AS, Al-Fahdawi S, Mohammed MA, Abdulkareem KH, Mostafa SA, Maashi MS, Arif M, Garcia-Zapirain B. COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images. Soft comput 2023; 27:2657-2672. [PMID: 33250662 PMCID: PMC7679792 DOI: 10.1007/s00500-020-05424-3] [Citation(s) in RCA: 61] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.
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Affiliation(s)
- Alaa S. Al-Waisy
- Communications Engineering Techniques Department, Information Technology Collage, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq
| | | | - Mazin Abed Mohammed
- College of Computer Science and Information Technology, University of Anbar, 11, Ramadi, Anbar, Iraq
| | | | - Salama A. Mostafa
- Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor Malaysia
| | - Mashael S. Maashi
- Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11451 Saudi Arabia
| | - Muhammad Arif
- School of Computer Science, Guangzhou University, Guangzhou, China
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Sekaran K, Polachirakkal Varghese R, Gnanasambandan R, Karthik G, Ramya I, George Priya Doss C. Molecular modeling of C1-inhibitor as SARS-CoV-2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study. Cell Biochem Funct 2023; 41:112-127. [PMID: 36517964 DOI: 10.1002/cbf.3769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/02/2022] [Accepted: 11/27/2022] [Indexed: 12/16/2022]
Abstract
The expeditious transmission of the severe acute respiratory coronavirus 2 (SARS-CoV-2), a strain of COVID-19, crumbled the global economic strength and caused a veritable collapse in health infrastructure. The molecular modeling of the novel coronavirus research sounds promising and equips more evidence about the pragmatic therapeutic options. This article proposes a machine-learning framework for identifying potential COVID-19 transcriptomic signatures. The transcriptomics data contains immune-related genes collected from multiple tissues (blood, nasal, and buccal) with accession number: GSE183071. Extensive bioinformatics work was carried out to identify the potential candidate markers, including differential expression analysis, protein interactions, gene ontology, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment studies. The overlapping investigation found SERPING1, the gene that encodes a glycosylated plasma protein C1-INH, in all three datasets. Furthermore, the immuno-informatics study was conducted on the C1-INH protein. 5DU3, the protein identifier of C1-INH, was fetched to identify the antigenicity, major histocompatibility (MHC) Class I and II binding epitopes, allergenicity, toxicity, and immunogenicity. The screening of peptides satisfying the vaccine-design criteria based on the metrics mentioned above is performed. The drug-gene interaction study reported that Rhucin is strongly associated with SERPING1. HSIC-Lasso (Hilbert-Schmidt independence criterion-least absolute shrinkage and selection operator), a model-free biomarker selection technique, was employed to identify the genes having a nonlinear relationship with the target class. The gene subset is trained with supervised machine learning models by a leave-one-out cross-validation method. Explainable artificial intelligence techniques perform the model interpretation analysis.
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Affiliation(s)
- Karthik Sekaran
- School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | | | - R Gnanasambandan
- School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
| | - G Karthik
- Department of Medicine, Christian Medical College, Vellore, India
| | - I Ramya
- Department of Medicine, Christian Medical College, Vellore, India
| | - C George Priya Doss
- School of Biosciences and Technology, Vellore Institute of Technology, Vellore, India
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Evolving deep convolutional neutral network by hybrid sine-cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images. Soft comput 2023; 27:3307-3326. [PMID: 33994846 PMCID: PMC8107782 DOI: 10.1007/s00500-021-05839-6] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/20/2021] [Indexed: 11/05/2022]
Abstract
The COVID19 pandemic globally and significantly has affected the life and health of many communities. The early detection of infected patients is effective in fighting COVID19. Using radiology (X-Ray) images is, perhaps, the fastest way to diagnose the patients. Thereby, deep Convolutional Neural Networks (CNNs) can be considered as applicable tools to diagnose COVID19 positive cases. Due to the complicated architecture of a deep CNN, its real-time training and testing become a challenging problem. This paper proposes using the Extreme Learning Machine (ELM) instead of the last fully connected layer to address this deficiency. However, the parameters' stochastic tuning of ELM's supervised section causes the final model unreliability. Therefore, to cope with this problem and maintain network reliability, the sine-cosine algorithm was utilized to tune the ELM's parameters. The designed network is then benchmarked on the COVID-Xray-5k dataset, and the results are verified by a comparative study with canonical deep CNN, ELM optimized by cuckoo search, ELM optimized by genetic algorithm, and ELM optimized by whale optimization algorithm. The proposed approach outperforms comparative benchmarks with a final accuracy of 98.83% on the COVID-Xray-5k dataset, leading to a relative error reduction of 2.33% compared to a canonical deep CNN. Even more critical, the designed network's training time is only 0.9421 ms and the overall detection test time for 3100 images is 2.721 s.
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Garai T, Garg H. Multi-criteria decision making of COVID-19 vaccines (in India) based on ranking interpreter technique under single valued bipolar neutrosophic environment. EXPERT SYSTEMS WITH APPLICATIONS 2022; 208:118160. [PMID: 35873110 PMCID: PMC9288936 DOI: 10.1016/j.eswa.2022.118160] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 05/31/2022] [Accepted: 07/08/2022] [Indexed: 06/01/2023]
Abstract
COVID-19 is a respiratory infection caused by a coronavirus that spreads from person to person. In the present situation, the COVID-19 pandemic is a swiftly rising phase. Now the time is the second wave ending phase of coronavirus and the third wave coming phase of coronavirus in India. The pandemic situation is moving forward all over India. Nowadays, the worldwide COVID-19 pandemic structure is a very hazardous situation. The COVID-19 vaccine can suppress this situation and gain preventive measures against coronavirus. In producing the COVID-19 vaccine, the Indian medical board plays a significant role. The COVID-19 vaccines have exhibited 90%-95% efficacy in preventing symptomatic COVID-19 infections. Against COVID-19, for emergency purposes, the Indian medical board has approved three vaccines: Covishield, Covaxin, and Sputnik V. Generally, the Indian people are embarrassed about the vaccination of COVID-19. All people are thinking about which vaccine is best for them. This labyrinth can be evaluated effectively using the multi-criteria decision-making (MCDM) technique. Therefore, we have proposed a novel MCDM technique for selecting COVID-19 vaccines. The main aim of this paper is to develop an MCDM technique based on a λ -weighted ranking interpreter ( R λ + , R λ - ). The first time, we have defined positive and negative λ -weighted rank interpreter for the ranking of single-valued bipolar neutrosophic (SVbN) number. Additionally, positive and negative λ -weighted values and positive and negative λ -weighted ambiguity of an SVbN-number are formulated here. Some important, valuable theorems and corollary of SVbN-number are formulated. To show the applicability of the proposed MCDM technique, we have considered a real decision-making problem where ratings of the alternatives are with SVbN-numbers.
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Affiliation(s)
- Totan Garai
- Department of Mathematics, Syamsundar College, Syamsundar, Purba Bardhaman 713424, West Bengal, India
| | - Harish Garg
- School of Mathematics, Thapar Institute of Engineering and Technology, Deemed University, Patiala 147004, Punjab, India
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Pramanik R, Dey S, Malakar S, Mirjalili S, Sarkar R. TOPSIS aided ensemble of CNN models for screening COVID-19 in chest X-ray images. Sci Rep 2022; 12:15409. [PMID: 36104401 PMCID: PMC9471038 DOI: 10.1038/s41598-022-18463-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 08/12/2022] [Indexed: 11/23/2022] Open
Abstract
The novel coronavirus (COVID-19), has undoubtedly imprinted our lives with its deadly impact. Early testing with isolation of the individual is the best possible way to curb the spread of this deadly virus. Computer aided diagnosis (CAD) provides an alternative and cheap option for screening of the said virus. In this paper, we propose a convolution neural network (CNN)-based CAD method for COVID-19 and pneumonia detection from chest X-ray images. We consider three input types for three identical base classifiers. To capture maximum possible complementary features, we consider the original RGB image, Red channel image and the original image stacked with Robert's edge information. After that we develop an ensemble strategy based on the technique for order preference by similarity to an ideal solution (TOPSIS) to aggregate the outcomes of base classifiers. The overall framework, called TOPCONet, is very light in comparison with standard CNN models in terms of the number of trainable parameters required. TOPCONet achieves state-of-the-art results when evaluated on the three publicly available datasets: (1) IEEE COVID-19 dataset + Kaggle Pneumonia Dataset, (2) Kaggle Radiography dataset and (3) COVIDx.
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MEF: Multidimensional Examination Framework for Prioritization of COVID-19 Severe Patients and Promote Precision Medicine Based on Hybrid Multi-Criteria Decision-Making Approaches. Bioengineering (Basel) 2022; 9:bioengineering9090457. [PMID: 36135003 PMCID: PMC9495842 DOI: 10.3390/bioengineering9090457] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/02/2022] [Accepted: 09/04/2022] [Indexed: 11/17/2022] Open
Abstract
Effective prioritization plays critical roles in precision medicine. Healthcare decisions are complex, involving trade-offs among numerous frequently contradictory priorities. Considering the numerous difficulties associated with COVID-19, approaches that could triage COVID-19 patients may help in prioritizing treatment and provide precise medicine for those who are at risk of serious disease. Prioritizing a patient with COVID-19 depends on a variety of examination criteria, but due to the large number of these biomarkers, it may be hard for medical practitioners and emergency systems to decide which cases should be given priority for treatment. The aim of this paper is to propose a Multidimensional Examination Framework (MEF) for the prioritization of COVID-19 severe patients on the basis of combined multi-criteria decision-making (MCDM) methods. In contrast to the existing literature, the MEF has not considered only a single dimension of the examination factors; instead, the proposed framework included different multidimensional examination criteria such as demographic, laboratory findings, vital signs, symptoms, and chronic conditions. A real dataset that consists of data from 78 patients with different examination criteria was used as a base in the construction of Multidimensional Evaluation Matrix (MEM). The proposed framework employs the CRITIC (CRiteria Importance Through Intercriteria Correlation) method to identify objective weights and importance for multidimensional examination criteria. Furthermore, the VIKOR (VIekriterijumsko KOmpromisno Rangiranje) method is utilized to prioritize COVID-19 severe patients. The results based on the CRITIC method showed that the most important examination criterion for prioritization is COVID-19 patients with heart disease, followed by cough and nasal congestion symptoms. Moreover, the VIKOR method showed that Patients 8, 3, 9, 59, and 1 are the most urgent cases that required the highest priority among the other 78 patients. Finally, the proposed framework can be used by medical organizations to prioritize the most critical COVID-19 patient that has multidimensional examination criteria and to promptly give appropriate care for more precise medicine.
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Sotoudeh-Anvari A. The applications of MCDM methods in COVID-19 pandemic: A state of the art review. Appl Soft Comput 2022; 126:109238. [PMID: 35795407 PMCID: PMC9245376 DOI: 10.1016/j.asoc.2022.109238] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 05/26/2022] [Accepted: 06/22/2022] [Indexed: 12/24/2022]
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Machine Learning-Based Multimodel Computing for Medical Imaging for Classification and Detection of Alzheimer Disease. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9211477. [PMID: 35990121 PMCID: PMC9391119 DOI: 10.1155/2022/9211477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 07/13/2022] [Accepted: 07/22/2022] [Indexed: 11/18/2022]
Abstract
Alzheimer is a disease that causes the brain to deteriorate over time. It starts off mild, but over the course of time, it becomes increasingly more severe. Alzheimer’s disease causes damage to brain cells as well as the death of those cells. Memory in humans is especially susceptible to this. Memory loss is the first indication of Alzheimer’s disease, but as the disease progresses and more brain cells die, additional symptoms arise. Medical image processing entails developing a visual portrayal of the inside of a body using a range of imaging technologies in order to discover and cure problems. This paper presents machine learning-based multimodel computing for medical imaging for classification and detection of Alzheimer disease. Images are acquired first. MRI images contain noise and contrast problem. Images are preprocessed using CLAHE algorithm. It improves image quality. CLAHE is better to other methods in its capacity to enhance the look of mammography in minute places. A white background makes the lesions more obvious to the naked eye. In spite of the fact that this method makes it simpler to differentiate between signal and noise, the images still include a significant amount of graininess. Images are segmented using the k-means algorithm. This results in the segmentation of images and identification of region of interest. Useful features are extracted using PCA algorithm. Finally, images are classified using machine learning algorithms.
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Patel RK, Kashyap M. Automated diagnosis of COVID stages from lung CT images using statistical features in 2-dimensional flexible analytic wavelet transform. Biocybern Biomed Eng 2022; 42:829-841. [PMID: 35791429 PMCID: PMC9247116 DOI: 10.1016/j.bbe.2022.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 06/15/2022] [Accepted: 06/18/2022] [Indexed: 11/18/2022]
Abstract
The COVID-19 epidemic has been causing a global problem since December 2019. COVID-19 is highly contagious and spreads rapidly throughout the world. Thus, early detection is essential. The progression of COVID-19 lung illness has been demonstrated to be aided by chest imaging. The respiratory system is the most vulnerable component of the human body to the COVID virus. COVID can be diagnosed promptly and accurately using images from a chest X-ray and a computed tomography scan. CT scans are preferred over X-rays to rule out other pulmonary illnesses, assist venous entry, and pinpoint any new heart problems. The traditional and trending tools are physical, time-inefficient, and not more accurate. Many techniques for detecting COVID utilizing CT scan images have recently been developed, yet none of them can efficiently detect COVID at an early stage. We proposed a two-dimensional Flexible analytical wavelet transform (FAWT) based on a novel technique in this work. This method is decomposed pre-processed images into sub-bands. Then statistical-based relevant features are extracted, and principal component analysis (PCA) is used to identify robust features. After that, robust features are ranked with the help of the Student's t-value algorithm. Finally, features are applied to Least Square-SVM (RBF) for classification. According to the experimental outcomes, our model beat state-of-the-art approaches for COVID classification. This model attained better classification accuracy of 93.47%, specificity 93.34%, sensitivity 93.6% and F1-score 0.93 using tenfold cross-validation.
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14
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Intelligent Model for Data Analytical Study of Coronavirus COVID-19 Databases. ELECTRONICS 2022. [DOI: 10.3390/electronics11131975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The pandemic coronavirus COVID-19 spread around the world with deaths exceeding that of SARS. COVID-19 is believed to have been transmitted from animals, especially from bats, and the virus is transmitted from person to person over time. This paper will help countries to make decisions that encourage access to corrected values and get some indication as to whether there are other factors that affect the spread of COVID-19, via methods such as by increasing the daily test rate. This paper presents an intelligent model for analyzing data collected from the countries affected by the COVID-19 virus. It considers the total number of tests that each country has undergone, the number of international tourist arrivals in each country, the percentage of employment, the life expectancy at birth, the median age, the population density, the number of people aged 65 years or older in millions, and the sex ratio. The proposed model is based on machine learning approaches using k-Means as a clustering approach, Support Vector Machine (SVM) as a classifier, and wrapper as a feature extraction approach. It consists of three phases of pre-processing the data collected, the discovery of outlier cases, the selection of the most effective features for each of the total infected, deaths, critical and recovery cases, and the construction of prediction models. Experimental results show that the extracted features of the wrapper technique have shown that it is more capable of fitting and predicting data than the Correlation-Based Feature Selection, Correlation Attribute Evaluation, Information Gain, and Relief Attribute Evaluation techniques. The SVM classifier also achieved the highest accuracy compared to other classification algorithms for predicting total infected, fatal, critical, and recovery cases.
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15
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Band SS, Ardabili S, Yarahmadi A, Pahlevanzadeh B, Kiani AK, Beheshti A, Alinejad-Rokny H, Dehzangi I, Chang A, Mosavi A, Moslehpour M. A Survey on Machine Learning and Internet of Medical Things-Based Approaches for Handling COVID-19: Meta-Analysis. Front Public Health 2022; 10:869238. [PMID: 35812486 PMCID: PMC9260273 DOI: 10.3389/fpubh.2022.869238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
Early diagnosis, prioritization, screening, clustering, and tracking of patients with COVID-19, and production of drugs and vaccines are some of the applications that have made it necessary to use a new style of technology to involve, manage, and deal with this epidemic. Strategies backed by artificial intelligence (A.I.) and the Internet of Things (IoT) have been undeniably effective to understand how the virus works and prevent it from spreading. Accordingly, the main aim of this survey is to critically review the ML, IoT, and the integration of IoT and ML-based techniques in the applications related to COVID-19, from the diagnosis of the disease to the prediction of its outbreak. According to the main findings, IoT provided a prompt and efficient approach to tracking the disease spread. On the other hand, most of the studies developed by ML-based techniques aimed at the detection and handling of challenges associated with the COVID-19 pandemic. Among different approaches, Convolutional Neural Network (CNN), Support Vector Machine, Genetic CNN, and pre-trained CNN, followed by ResNet have demonstrated the best performances compared to other methods.
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Affiliation(s)
- Shahab S. Band
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Sina Ardabili
- Department of Informatics, J. Selye University, Komárom, Slovakia
| | - Atefeh Yarahmadi
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Bahareh Pahlevanzadeh
- Department of Design and System Operations, Regional Information Center for Science and Technology (R.I.C.E.S.T.), Shiraz, Iran
| | - Adiqa Kausar Kiani
- Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Taiwan
| | - Amin Beheshti
- Department of Computing, Macquarie University, Sydney, NSW, Australia
| | - Hamid Alinejad-Rokny
- BioMedical Machine Learning Lab, The Graduate School of Biomedical Engineering, U.N.S.W. Sydney, Sydney, NSW, Australia
- U.N.S.W. Data Science Hub, The University of New South Wales (U.N.S.W. Sydney), Sydney, NSW, Australia
- Health Data Analytics Program, AI-enabled Processes (A.I.P.) Research Centre, Macquarie University, Sydney, NSW, Australia
| | - Iman Dehzangi
- Department of Computer Science, Rutgers University, Camden, NJ, United States
- Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, United States
| | - Arthur Chang
- Bachelor Program in Interdisciplinary Studies, National Yunlin University of Science and Technology, Douliu, Taiwan
| | - Amir Mosavi
- John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary
- Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia
| | - Massoud Moslehpour
- Department of Business Administration, College of Management, Asia University, Taichung, Taiwan
- Department of Management, California State University, San Bernardino, CA, United States
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Alsalem MA, Mohammed R, Albahri OS, Zaidan AA, Alamoodi AH, Dawood K, Alnoor A, Albahri AS, Zaidan BB, Aickelin U, Alsattar H, Alazab M, Jumaah F. Rise of multiattribute decision-making in combating COVID-19: A systematic review of the state-of-the-art literature. INT J INTELL SYST 2022; 37:3514-3624. [PMID: 38607836 PMCID: PMC8653072 DOI: 10.1002/int.22699] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Revised: 09/08/2021] [Accepted: 09/16/2021] [Indexed: 12/17/2022]
Abstract
Considering the coronavirus disease 2019 (COVID-19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision-making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID-19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID-19 by presenting a systematic literature review of the state-of-the-art COVID-19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical (n = 30), social (n = 4), economic (n = 13) and technological (n = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID-19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.
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Affiliation(s)
- Mohammed Assim Alsalem
- Department of Computing, Faculty of Arts, Computing and Creative IndustryUniversiti Pendidikan Sultan IdrisTanjung MalimMalaysia
| | - Rawia Mohammed
- Faculty of Computing and Innovative TechnologyGeomatika University CollegeKuala LumpurMalaysia
| | - Osamah Shihab Albahri
- Department of Computing, Faculty of Arts, Computing and Creative IndustryUniversiti Pendidikan Sultan IdrisTanjung MalimMalaysia
| | - Aws Alaa Zaidan
- Department of Computing, Faculty of Arts, Computing and Creative IndustryUniversiti Pendidikan Sultan IdrisTanjung MalimMalaysia
| | - Abdullah Hussein Alamoodi
- Department of Computing, Faculty of Arts, Computing and Creative IndustryUniversiti Pendidikan Sultan IdrisTanjung MalimMalaysia
| | - Kareem Dawood
- Computer Science DepartmentKomar University of Science and Technology (KUST)SulaymaniyahIraq
| | - Alhamzah Alnoor
- School of ManagementUniversiti Sains MalaysiaPulau PinangMalaysia
| | - Ahmed Shihab Albahri
- Informatics Institute for Postgraduate Studies (IIPS)Iraqi Commission for Computers and Informatics (ICCI)BaghdadIraq
| | - Bilal Bahaa Zaidan
- Future Technology Research CenterNational Yunlin University of Science and TechnologyDouliouTaiwan R.O.C.
| | - Uwe Aickelin
- School of Computing and Information SystemsThe University of MelbourneAustralia
| | - Hassan Alsattar
- Department of Computing, Faculty of Arts, Computing and Creative IndustryUniversiti Pendidikan Sultan IdrisTanjung MalimMalaysia
| | - Mamoun Alazab
- College of Engineering, IT and EnvironmentCharles Darwin UniversityCasuarinaNorthern TerritoryAustralia
| | - Fawaz Jumaah
- Department of Advanced Applications and Embedded SystemsIntel CorporationPulau PinangMalaysia
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17
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Chowdhury NK, Kabir MA, Rahman MM, Islam SMS. Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method. Comput Biol Med 2022; 145:105405. [PMID: 35318171 PMCID: PMC8926945 DOI: 10.1016/j.compbiomed.2022.105405] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 12/16/2022]
Abstract
This research aims to analyze the performance of state-of-the-art machine learning techniques for classifying COVID-19 from cough sounds and to identify the model(s) that consistently perform well across different cough datasets. Different performance evaluation metrics (precision, sensitivity, specificity, AUC, accuracy, etc.) make selecting the best performance model difficult. To address this issue, in this paper, we propose an ensemble-based multi-criteria decision making (MCDM) method for selecting top performance machine learning technique(s) for COVID-19 cough classification. We use four cough datasets, namely Cambridge, Coswara, Virufy, and NoCoCoDa to verify the proposed method. At first, our proposed method uses the audio features of cough samples and then applies machine learning (ML) techniques to classify them as COVID-19 or non-COVID-19. Then, we consider a multi-criteria decision-making (MCDM) method that combines ensemble technologies (i.e., soft and hard) to select the best model. In MCDM, we use the technique for order preference by similarity to ideal solution (TOPSIS) for ranking purposes, while entropy is applied to calculate evaluation criteria weights. In addition, we apply the feature reduction process through recursive feature elimination with cross-validation under different estimators. The results of our empirical evaluations show that the proposed method outperforms the state-of-the-art models. We see that when the proposed method is used for analysis using the Extra-Trees classifier, it has achieved promising results (AUC: 0.95, Precision: 1, Recall: 0.97).
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Affiliation(s)
- Nihad Karim Chowdhury
- Department of Computer Science and Engineering, University of Chittagong, Bangladesh,Corresponding author
| | - Muhammad Ashad Kabir
- Data Science Research Unit, School of Computing, Mathematics and Engineering, Charles Sturt University, NSW, Australia
| | - Md. Muhtadir Rahman
- Department of Computer Science and Engineering, University of Chittagong, Bangladesh
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18
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Evaluating building performance in healthcare facilities using entropy and graph heuristic theories. Sci Rep 2022; 12:8973. [PMID: 35643727 PMCID: PMC9148309 DOI: 10.1038/s41598-022-13004-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 05/17/2022] [Indexed: 11/27/2022] Open
Abstract
Designing a healthcare facility is one of the most challenging tasks due to the complexity associated with these facilities. The primary goal of healthcare facilities is to provide high-quality care; consequently, the design of healthcare facilities and their environments directly affects the facility's productivity, the organization's economic performance, the experienced clinical outcomes in the hospital, as well as patient and staff satisfaction. The redesign of a healthcare facility is essential for ensuring a serene healing environment for the patients and thus influences their healing rates, reduces the amount of time spent in the facilities, and impacts their level of satisfaction with the care provided. The evaluation methodology is a step in the redesign process that measures the performance of healthcare buildings according to international standards. In this study, the collected standards were weighted using an entropy algorithm to evaluate different departments in various hospitals. In addition, the layout score was measured using the adjacent algorithm as one of the graph heuristic methods to determine whether the department or the whole hospital can be redesigned to meet international standards. According to the results of our methodologies being used in one selected hospital in Egypt, the average of the satisfied standards was 43%, standards that could be satisfied were 24%, not applicable standards were 34%, and the average layout score was 25.
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19
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BeCaked: An Explainable Artificial Intelligence Model for COVID-19 Forecasting. Sci Rep 2022; 12:7969. [PMID: 35562369 PMCID: PMC9105619 DOI: 10.1038/s41598-022-11693-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 04/26/2022] [Indexed: 12/02/2022] Open
Abstract
From the end of 2019, one of the most serious and largest spread pandemics occurred in Wuhan (China) named Coronavirus (COVID-19). As reported by the World Health Organization, there are currently more than 100 million infectious cases with an average mortality rate of about five percent all over the world. To avoid serious consequences on people’s lives and the economy, policies and actions need to be suitably made in time. To do that, the authorities need to know the future trend in the development process of this pandemic. This is the reason why forecasting models play an important role in controlling the pandemic situation. However, the behavior of this pandemic is extremely complicated and difficult to be analyzed, so that an effective model is not only considered on accurate forecasting results but also the explainable capability for human experts to take action pro-actively. With the recent advancement of Artificial Intelligence (AI) techniques, the emerging Deep Learning (DL) models have been proving highly effective when forecasting this pandemic future from the huge historical data. However, the main weakness of DL models is lacking the explanation capabilities. To overcome this limitation, we introduce a novel combination of the Susceptible-Infectious-Recovered-Deceased (SIRD) compartmental model and Variational Autoencoder (VAE) neural network known as BeCaked. With pandemic data provided by the Johns Hopkins University Center for Systems Science and Engineering, our model achieves 0.98 \documentclass[12pt]{minimal}
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\begin{document}$$R^2$$\end{document}R2 and 0.012 MAPE at world level with 31-step forecast and up to 0.99 \documentclass[12pt]{minimal}
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\begin{document}$$R^2$$\end{document}R2 and 0.0026 MAPE at country level with 15-step forecast on predicting daily infectious cases. Not only enjoying high accuracy, but BeCaked also offers useful justifications for its results based on the parameters of the SIRD model. Therefore, BeCaked can be used as a reference for authorities or medical experts to make on time right decisions.
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Radulescu CZ, Radulescu M, Boncea R. A Multi-Criteria Decision Support and Application to the Evaluation of the Fourth Wave of COVID-19 Pandemic. ENTROPY 2022; 24:e24050642. [PMID: 35626527 PMCID: PMC9141305 DOI: 10.3390/e24050642] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/28/2022] [Accepted: 04/30/2022] [Indexed: 12/10/2022]
Abstract
The COVID-19 pandemic caused important health and societal damage across the world in 2020–2022. Its study represents a tremendous challenge for the scientific community. The correct evaluation and analysis of the situation can lead to the elaboration of the most efficient strategies and policies to control and mitigate its propagation. The paper proposes a Multi-Criteria Decision Support (MCDS) based on the combination of three methods: the Group Analytic Hierarchy Process (GAHP), which is a subjective group weighting method; Extended Entropy Weighting Method (EEWM), which is an objective weighting method; and the COmplex PRoportional ASsessment (COPRAS), which is a multi-criteria method. The COPRAS uses the combined weights calculated by the GAHP and EEWM. The sum normalization (SN) is considered for COPRAS and EEWM. An extended entropy is proposed in EEWM. The MCDS is implemented for the development of a complex COVID-19 indicator called COVIND, which includes several countries’ COVID-19 indicators, over a fourth COVID-19 wave, for a group of European countries. Based on these indicators, a ranking of the countries is obtained. An analysis of the obtained rankings is realized by the variation of two parameters: a parameter that describes the combination of weights obtained with EEWM and GAHP and the parameter of extended entropy function. A correlation analysis between the new indicator and the general country indicators is performed. The MCDS provides policy makers with a decision support able to synthesize the available information on the fourth wave of the COVID-19 pandemic.
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Affiliation(s)
- Constanta Zoie Radulescu
- National Institute for Research and Development in Informatics, 8-10, Mareşal Averescu, 011455 Bucharest, Romania; (C.Z.R.); (R.B.)
| | - Marius Radulescu
- “Gheorghe Mihoc-Caius Iacob” Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy, Calea 13 Septembrie, No. 13, 050711 Bucharest, Romania
- Correspondence:
| | - Radu Boncea
- National Institute for Research and Development in Informatics, 8-10, Mareşal Averescu, 011455 Bucharest, Romania; (C.Z.R.); (R.B.)
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21
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Let AI Perform Better Next Time—A Systematic Review of Medical Imaging-Based Automated Diagnosis of COVID-19: 2020–2022. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12083895] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically. Due to the false-negative rate and the time-consuming characteristic of the Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, diagnosing based on X-ray images and Computed Tomography (CT) images has been widely adopted to confirm positive COVID-19 RT-PCR tests. Since the very beginning of the pandemic, researchers in the artificial intelligence area have proposed a large number of automatic diagnosing models, hoping to assist radiologists and improve the diagnosing accuracy. However, after two years of development, there are still few models that can actually be applied in real-world scenarios. Numerous problems have emerged in the research of the automated diagnosis of COVID-19. In this paper, we present a systematic review of these diagnosing models. A total of 179 proposed models are involved. First, we compare the medical image modalities (CT or X-ray) for COVID-19 diagnosis from both the clinical perspective and the artificial intelligence perspective. Then, we classify existing methods into two types—image-level diagnosis (i.e., classification-based methods) and pixel-level diagnosis (i.e., segmentation-based models). For both types of methods, we define universal model pipelines and analyze the techniques that have been applied in each step of the pipeline in detail. In addition, we also review some commonly adopted public COVID-19 datasets. More importantly, we present an in-depth discussion of the existing automated diagnosis models and note a total of three significant problems: biased model performance evaluation; inappropriate implementation details; and a low reproducibility, reliability and explainability. For each point, we give corresponding recommendations on how we can avoid making the same mistakes and let AI perform better in the next pandemic.
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22
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The Determinants of Outward Foreign Direct Investment from Latin America and the Caribbean: An Integrated Entropy-Based TOPSIS Multiple Regression Analysis Framework. JOURNAL OF RISK AND FINANCIAL MANAGEMENT 2022. [DOI: 10.3390/jrfm15030130] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Given that home country factors play a major role in the internationalization of emerging market firms, there is an ever-growing debate on how they influence the intensity of outward foreign direct investment (OFDI) from these regions. This study investigates how home country factors affect the OFDI intensity in Latin America and Caribbean (LAC) countries. We use the entropy weight method, which uses the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method and a balanced panel data consisting of 19 countries from 2007 to 2016. The results show a positive association between macroeconomic performance, formal institutions, infrastructure, technology and the OFDI intensity. Furthermore, we find that robust formal institutions, along with the quality of infrastructure and technology, positively moderate the relationship between macroeconomic performance and the OFDI intensity. These findings show that the internationalization of LAC firms is highly dependent on the contextual conditions in their markets.
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23
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Development of a computer-aided tool for detection of COVID-19 pneumonia from CXR images using machine learning algorithm. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2022. [PMCID: PMC8841229 DOI: 10.1016/j.jrras.2022.02.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The novel coronavirus (SARS-CoV-2) is spreading rapidly worldwide, and it has become a greater risk for human beings. To curb the community transmission of this virus, rapid detection and identification of the affected people via a quick diagnostic process are necessary. Media studies have shown that most COVID-19 victims endure lung disease. For rapid identification of the affected patient, chest CT scans and X-ray images have been reported to be suitable techniques. However, chest X-ray (CXR) shows more convenience than the CT imaging techniques because it has faster imaging times than CT and is also simple and cost-effective. Literature shows that transfer learning is one of the most successful techniques to analyze chest X-ray images and correctly identify various types of pneumonia. Since SVM has a remarkable aspect that tremendously provides good results using a small data set thus in this study we have used SVM machine learning algorithm to diagnose COVID-19 from chest X-ray images. The image processing tool called RGB and SqueezeNet models were used to get more images to diagnose the available data set. Our adopted model shows an accuracy of 98.8% to detect the COVID-19 affected patient from CXR images. It is expected that our proposed computer-aided detection tool (CAT) will play a key role in reducing the spread of infectious diseases in society through a faster patient screening process.
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24
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Alyasseri ZAA, Al‐Betar MA, Doush IA, Awadallah MA, Abasi AK, Makhadmeh SN, Alomari OA, Abdulkareem KH, Adam A, Damasevicius R, Mohammed MA, Zitar RA. Review on COVID-19 diagnosis models based on machine learning and deep learning approaches. EXPERT SYSTEMS 2022; 39:e12759. [PMID: 34511689 PMCID: PMC8420483 DOI: 10.1111/exsy.12759] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 05/17/2021] [Accepted: 06/07/2021] [Indexed: 05/02/2023]
Abstract
COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.
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Affiliation(s)
- Zaid Abdi Alkareem Alyasseri
- Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
- ECE Department‐Faculty of EngineeringUniversity of KufaNajafIraq
| | - Mohammed Azmi Al‐Betar
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Department of Information TechnologyAl‐Huson University College, Al‐Balqa Applied UniversityIrbidJordan
| | - Iyad Abu Doush
- Computing Department, College of Engineering and Applied SciencesAmerican University of KuwaitSalmiyaKuwait
- Computer Science DepartmentYarmouk UniversityIrbidJordan
| | - Mohammed A. Awadallah
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Department of Computer ScienceAl‐Aqsa UniversityGazaPalestine
| | - Ammar Kamal Abasi
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- School of Computer SciencesUniversiti Sains MalaysiaPenangMalaysia
| | - Sharif Naser Makhadmeh
- Artificial Intelligence Research Center (AIRC)Ajman UniversityAjmanUnited Arab Emirates
- Faculty of Information TechnologyMiddle East UniversityAmmanJordan
| | | | | | - Afzan Adam
- Center for Artificial Intelligence Technology, Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiMalaysia
| | | | - Mazin Abed Mohammed
- College of Computer Science and Information TechnologyUniversity of AnbarAnbarIraq
| | - Raed Abu Zitar
- Sorbonne Center of Artificial IntelligenceSorbonne University‐Abu DhabiAbu DhabiUnited Arab Emirates
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25
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Yaşar H, Ceylan M. A novel study for automatic two-class COVID-19 diagnosis (between COVID-19 and Healthy, Pneumonia) on X-ray images using texture analysis and 2-D/3-D convolutional neural networks. MULTIMEDIA SYSTEMS 2022; 29:1-19. [PMID: 35125671 PMCID: PMC8799982 DOI: 10.1007/s00530-022-00892-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 01/07/2022] [Indexed: 06/14/2023]
Abstract
The pandemic caused by the COVID-19 virus affects the world widely and heavily. When examining the CT, X-ray, and ultrasound images, radiologists must first determine whether there are signs of COVID-19 in the images. That is, COVID-19/Healthy detection is made. The second determination is the separation of pneumonia caused by the COVID-19 virus and pneumonia caused by a bacteria or virus other than COVID-19. This distinction is key in determining the treatment and isolation procedure to be applied to the patient. In this study, which aims to diagnose COVID-19 early using X-ray images, automatic two-class classification was carried out in four different titles: COVID-19/Healthy, COVID-19 Pneumonia/Bacterial Pneumonia, COVID-19 Pneumonia/Viral Pneumonia, and COVID-19 Pneumonia/Other Pneumonia. For this study, 3405 COVID-19, 2780 Bacterial Pneumonia, 1493 Viral Pneumonia, and 1989 Healthy images obtained by combining eight different data sets with open access were used. In the study, besides using the original X-ray images alone, classification results were obtained by accessing the images obtained using Local Binary Pattern (LBP) and Local Entropy (LE). The classification procedures were repeated for the images that were combined with the original images, LBP, and LE images in various combinations. 2-D CNN (Two-Dimensional Convolutional Neural Networks) and 3-D CNN (Three-Dimensional Convolutional Neural Networks) architectures were used as classifiers within the scope of the study. Mobilenetv2, Resnet101, and Googlenet architectures were used in the study as a 2-D CNN. A 24-layer 3-D CNN architecture has also been designed and used. Our study is the first to analyze the effect of diversification of input data type on classification results of 2-D/3-D CNN architectures. The results obtained within the scope of the study indicate that diversifying X-ray images with tissue analysis methods in the diagnosis of COVID-19 and including CNN input provides significant improvements in the results. Also, it is understood that the 3-D CNN architecture can be an important alternative to achieve a high classification result.
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Affiliation(s)
- Huseyin Yaşar
- Ministry of Health of Republic of Turkey, Ankara, Turkey
| | - Murat Ceylan
- Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences, Konya Technical University, Konya, Turkey
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26
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Multi-criteria decision-making for coronavirus disease 2019 applications: a theoretical analysis review. Artif Intell Rev 2022; 55:4979-5062. [PMID: 35103030 PMCID: PMC8791811 DOI: 10.1007/s10462-021-10124-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The influence of the ongoing COVID-19 pandemic that is being felt in all spheres of our lives and has a remarkable effect on global health care delivery occurs amongst the ongoing global health crisis of patients and the required services. From the time of the first detection of infection amongst the public, researchers investigated various applications in the fight against the COVID-19 outbreak and outlined the crucial roles of different research areas in this unprecedented battle. In the context of existing studies in the literature surrounding COVID-19, related to medical treatment decisions, the dimensions of context addressed in previous multidisciplinary studies reveal the lack of appropriate decision mechanisms during the COVID-19 outbreak. Multiple criteria decision making (MCDM) has been applied widely in our daily lives in various ways with numerous successful stories to help analyse complex decisions and provide an accurate decision process. The rise of MCDM in combating COVID-19 from a theoretical perspective view needs further investigation to meet the important characteristic points that match integrating MCDM and COVID-19. To this end, a comprehensive review and an analysis of these multidisciplinary fields, carried out by different MCDM theories concerning COVID19 in complex case studies, are provided. Research directions on exploring the potentials of MCDM and enhancing its capabilities and power through two directions (i.e. development and evaluation) in COVID-19 are thoroughly discussed. In addition, Bibliometrics has been analysed, visualization and interpretation based on the evaluation and development category using R-tool involves; annual scientific production, country scientific production, Wordcloud, factor analysis in bibliographic, and country collaboration map. Furthermore, 8 characteristic points that go through the analysis based on new tables of information are highlighted and discussed to cover several important facts and percentages associated with standardising the evaluation criteria, MCDM theory in ranking alternatives and weighting criteria, operators used with the MCDM methods, normalisation types for the data used, MCDM theory contexts, selected experts ways, validation scheme for effective MCDM theory and the challenges of MCDM theory used in COVID-19 studies. Accordingly, a recommended MCDM theory solution is presented through three distinct phases as a future direction in COVID19 studies. Key phases of this methodology include the Fuzzy Delphi method for unifying criteria and establishing importance level, Fuzzy weighted Zero Inconsistency for weighting to mitigate the shortcomings of the previous weighting techniques and the MCDM approach by the name Fuzzy Decision by Opinion Score method for prioritising alternatives and providing a unique ranking solution. This study will provide MCDM researchers and the wider community an overview of the current status of MCDM evaluation and development methods and motivate researchers in harnessing MCDM potentials in tackling an accurate decision for different fields against COVID-19.
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27
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Rahmani AM, Azhir E, Naserbakht M, Mohammadi M, Aldalwie AHM, Majeed MK, Taher Karim SH, Hosseinzadeh M. Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:28779-28798. [PMID: 35382107 PMCID: PMC8970643 DOI: 10.1007/s11042-022-12952-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 05/09/2021] [Accepted: 03/10/2022] [Indexed: 05/04/2023]
Abstract
Since early 2020, Coronavirus Disease 2019 (COVID-19) has spread widely around the world. COVID-19 infects the lungs, leading to breathing difficulties. Early detection of COVID-19 is important for the prevention and treatment of pandemic. Numerous sources of medical images (e.g., Chest X-Rays (CXR), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI)) are regarded as a desirable technique for diagnosing COVID-19 cases. Medical images of coronavirus patients show that the lungs are filled with sticky mucus that prevents them from inhaling. Today, Artificial Intelligence (AI) based algorithms have made a significant shift in the computer aided diagnosis due to their effective feature extraction capabilities. In this survey, a complete and systematic review of the application of Machine Learning (ML) methods for the detection of COVID-19 is presented, focused on works that used medical images. We aimed to evaluate various ML-based techniques in detecting COVID-19 using medical imaging. A total of 26 papers were extracted from ACM, ScienceDirect, Springerlink, Tech Science Press, and IEEExplore. Five different ML categories to review these mechanisms are considered, which are supervised learning-based, deep learning-based, active learning-based, transfer learning-based, and evolutionary learning-based mechanisms. A number of articles are investigated in each group. Also, some directions for further research are discussed to improve the detection of COVID-19 using ML techniques in the future. In most articles, deep learning is used as the ML method. Also, most of the researchers used CXR images to diagnose COVID-19. Most articles reported accuracy of the models to evaluate model performance. The accuracy of the studied models ranged from 0.84 to 0.99. The studies demonstrated the current status of AI techniques in using AI potentials in the fight against COVID-19.
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Affiliation(s)
- Amir Masoud Rahmani
- Future Technology Research Center, National Yunlin University of Science and Technology, Douliu, Yunlin Taiwan
| | - Elham Azhir
- Research and Development Center, Mobile Telecommunication Company of Iran, Tehran, Iran
| | - Morteza Naserbakht
- Mental Health Research Center, Psychosocial Health Research Institute, Iran University of Medical Sciences, Tehran, Iran
| | - Mokhtar Mohammadi
- Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Kurdistan Region, Iraq
| | - Adil Hussein Mohammed Aldalwie
- Department of Communication and Computer Engineering, Faculty of Engineering, Cihan University-Erbil, Kurdistan Region, Iraq
| | - Mohammed Kamal Majeed
- Information Technology Department, Faculty of Applied Science, Tishk International University, Erbil, Iraq
| | - Sarkhel H. Taher Karim
- Computer Department, College of Science, University of Halabja, Halabja, Iraq
- Computer Networks Department, Sulaimani Polytechnic University, Technical College of Informatics, Sulaymaniyah, Iraq
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28
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Hasoon JN, Fadel AH, Hameed RS, Mostafa SA, Khalaf BA, Mohammed MA, Nedoma J. COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images. RESULTS IN PHYSICS 2021; 31:105045. [PMID: 34840938 PMCID: PMC8607738 DOI: 10.1016/j.rinp.2021.105045] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 11/19/2021] [Accepted: 11/19/2021] [Indexed: 05/03/2023]
Abstract
The term COVID-19 is an abbreviation of Coronavirus 2019, which is considered a global pandemic that threatens the lives of millions of people. Early detection of the disease offers ample opportunity of recovery and prevention of spreading. This paper proposes a method for classification and early detection of COVID-19 through image processing using X-ray images. A set of procedures are applied, including preprocessing (image noise removal, image thresholding, and morphological operation), Region of Interest (ROI) detection and segmentation, feature extraction, (Local binary pattern (LBP), Histogram of Gradient (HOG), and Haralick texture features) and classification (K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)). The combinations of the feature extraction operators and classifiers results in six models, namely LBP-KNN, HOG-KNN, Haralick-KNN, LBP-SVM, HOG-SVM, and Haralick-SVM. The six models are tested based on test samples of 5,000 images with the percentage of training of 5-folds cross-validation. The evaluation results show high diagnosis accuracy from 89.2% up to 98.66%. The LBP-KNN model outperforms the other models in which it achieves an average accuracy of 98.66%, a sensitivity of 97.76%, specificity of 100%, and precision of 100%. The proposed method for early detection and classification of COVID-19 through image processing using X-ray images is proven to be usable in which it provides an end-to-end structure without the need for manual feature extraction and manual selection methods.
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Affiliation(s)
- Jamal N Hasoon
- Department of Computer Science, Mustansiriyah University, 10001 Baghdad, Iraq
| | - Ali Hussein Fadel
- Department of Computer Science, University of Diyala, 32001 Diyala, Iraq
| | - Rasha Subhi Hameed
- Department of Computer Science, University of Diyala, 32001 Diyala, Iraq
| | - Salama A Mostafa
- Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Johor, Malaysia
| | - Bashar Ahmed Khalaf
- Department of Medical Instruments Engineering Techniques, Bilad Alrafidain University College, 32001 Diyala, Iraq
| | - Mazin Abed Mohammed
- College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq
| | - Jan Nedoma
- Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic
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29
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Gül S. Fermatean fuzzy set extensions of SAW, ARAS, and VIKOR with applications in COVID-19 testing laboratory selection problem. EXPERT SYSTEMS 2021; 38:e12769. [PMID: 34511690 PMCID: PMC8420344 DOI: 10.1111/exsy.12769] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 05/31/2021] [Accepted: 06/23/2021] [Indexed: 05/09/2023]
Abstract
The multiple attribute decision-making models are empowered with the support of fuzzy sets such as intuitionistic, q-rung orthopair, Pythagorean, and picture fuzzy sets, and also neutrosophic sets, etc. These concepts generate varying representation opportunities for the decision-maker's preferences and expertise. Pythagorean and Fermatean fuzzy sets are special cases of q-rung orthopair fuzzy set when q = 2 and q = 3, respectively. From a geometric perspective, the latter provides a broader representation domain than the former does. In this study, the emerging concept of Fermatean fuzzy set is studied in detail and three well-known multi-attribute evaluation methods, namely SAW, ARAS, and VIKOR are extended under Fermatean fuzzy environment. In this manner, the decision-makers will have more freedom in specifying their preferences, thoughts, and expertise, and the abovementioned decision approaches will be able to handle this new type of data. The applicability of the propositions is shown in determining the best Covid-19 testing laboratory which is an important topic of the ongoing global health crisis. To validate the proposed methods, a benchmark analysis covering the results of the existing Fermatean fuzzy set-based decision methods, namely TOPSIS, WPM, and Yager aggregation operators is presented.
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Affiliation(s)
- Sait Gül
- Faculty of Engineering and Natural Sciences, Management Engineering DepartmentBahçeşehir UniversityBeşiktaş, İstanbul34353Turkey
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30
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Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:9269173. [PMID: 34795794 PMCID: PMC8594989 DOI: 10.1155/2021/9269173] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 09/14/2021] [Accepted: 10/21/2021] [Indexed: 01/01/2023]
Abstract
Early diagnosis of the harmful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with clinical expertise, allows governments to break the transition chain and flatten the epidemic curve. Although reverse transcription-polymerase chain reaction (RT-PCR) offers quick results, chest X-ray (CXR) imaging is a more reliable method for disease classification and assessment. The rapid spread of the coronavirus disease 2019 (COVID-19) has triggered extensive research towards developing a COVID-19 detection toolkit. Recent studies have confirmed that the deep learning-based approach, such as convolutional neural networks (CNNs), provides an optimized solution for COVID-19 classification; however, they require substantial training data for learning features. Gathering this training data in a short period has been challenging during the pandemic. Therefore, this study proposes a new model of CNN and deep convolutional generative adversarial networks (DCGANs) that classify CXR images into normal, pneumonia, and COVID-19. The proposed model contains eight convolutional layers, four max-pooling layers, and two fully connected layers, which provide better results than the existing pretrained methods (AlexNet and GoogLeNet). DCGAN performs two tasks: (1) generating synthetic/fake images to overcome the challenges of an imbalanced dataset and (2) extracting deep features of all images in the dataset. In addition, it enlarges the dataset and represents the characteristics of diversity to provide a good generalization effect. In the experimental analysis, we used four distinct publicly accessible datasets of chest X-ray images (COVID-19 X-ray, COVID Chest X-ray, COVID-19 Radiography, and CoronaHack-Chest X-Ray) to train and test the proposed CNN and the existing pretrained methods. Thereafter, the proposed CNN method was trained with the four datasets based on the DCGAN synthetic images, resulting in higher accuracy (94.8%, 96.6%, 98.5%, and 98.6%) than the existing pretrained models. The overall results suggest that the proposed DCGAN-CNN approach is a promising solution for efficient COVID-19 diagnosis.
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31
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An Expert System for COVID-19 Infection Tracking in Lungs Using Image Processing and Deep Learning Techniques. BIOMED RESEARCH INTERNATIONAL 2021; 2021:1896762. [PMID: 34782860 PMCID: PMC8590595 DOI: 10.1155/2021/1896762] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 09/29/2021] [Accepted: 10/16/2021] [Indexed: 11/17/2022]
Abstract
The proposed method introduces algorithms for the preprocessing of normal, COVID-19, and pneumonia X-ray lung images which promote the accuracy of classification when compared with raw (unprocessed) X-ray lung images. Preprocessing of an image improves the quality of an image increasing the intersection over union scores in segmentation of lungs from the X-ray images. The authors have implemented an efficient preprocessing and classification technique for respiratory disease detection. In this proposed method, the histogram of oriented gradients (HOG) algorithm, Haar transform (Haar), and local binary pattern (LBP) algorithm were applied on lung X-ray images to extract the best features and segment the left lung and right lung. The segmentation of lungs from the X-ray can improve the accuracy of results in COVID-19 detection algorithms or any machine/deep learning techniques. The segmented lungs are validated over intersection over union scores to compare the algorithms. The preprocessed X-ray image results in better accuracy in classification for all three classes (normal/COVID-19/pneumonia) than unprocessed raw images. VGGNet, AlexNet, Resnet, and the proposed deep neural network were implemented for the classification of respiratory diseases. Among these architectures, the proposed deep neural network outperformed the other models with better classification accuracy.
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32
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Forestal RL, Pi S. A hybrid approach based on
ELECTRE III
‐genetic algorithm and
TOPSIS
method for selection of optimal
COVID
‐19 vaccines. JOURNAL OF MULTI-CRITERIA DECISION ANALYSIS 2021. [PMCID: PMC8646624 DOI: 10.1002/mcda.1772] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
COVID‐19 pandemic poses unprecedented challenges to the world health system, prompting academics and health professionals to develop appropriate solutions. Researchers reported different COVID‐19 vaccines introduced by institutions and companies around the globe, which are at different stages of development. However, research developing an integrated framework for selecting and ranking the optimal potential vaccine against COVID‐19 is minimal. This paper aimed to fill this gap by using a hybrid methodology based on ELimination Et Choice Translating REality III (ELECTRE III)–Genetic Algorithm (GA) and Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) approach to select the optimal SARS‐CoV‐2 vaccine. ELECTRE III method yields a fathomable analysis of the concordance index, while GA is known for its ability to disaggregate decision‐making preferences from holistic decisions. TOPSIS is preferred for picking an ideal and an anti‐ideal solution. Thus, combining ELECTRE III‐GA and TOPSIS is considered the best model to assess vaccines against the pandemic. The results confirm that the best vaccines rely on a high level of safety, efficacy, and availability. Our developed evaluation framework can help healthcare professionals and researchers gain research information and make critical decisions regarding potential vaccines against the disease.
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Affiliation(s)
| | - Shih‐Ming Pi
- Department of Information Management Chung Yuan Christian University Taoyuan Taiwan
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33
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A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks. APPL INTELL 2021; 51:2740-2763. [PMID: 34764560 PMCID: PMC7609830 DOI: 10.1007/s10489-020-02019-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/10/2020] [Indexed: 12/17/2022]
Abstract
In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelligence method, was used, and automatic classification of images was performed using convolutional neural networks (CNN). In the first training-test data set used in the study, there were 230 X-ray images, of which 150 were Covid-19 and 80 were non-Covid-19, while in the second training-test data set there were 476 X-ray images, of which 150 were Covid-19 and 326 were non-Covid-19. Thus, classification results have been provided for two data sets, containing predominantly Covid-19 images and predominantly non-Covid-19 images, respectively. In the study, a 23-layer CNN architecture and a 54-layer CNN architecture were developed. Within the scope of the study, the results were obtained using chest X-ray images directly in the training-test procedures and the sub-band images obtained by applying dual tree complex wavelet transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying local binary pattern (LBP) to the chest X-ray images. Within the scope of the study, four new result generation pipeline algorithms having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in this study, the training sessions were carried out using the k-fold cross validation method. Here the k value was chosen as 23 for the first and second training-test data sets. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy, F-1 score, and area under the receiver operating characteristic curve (AUC) for the first training-test data set were 0,9947, 0,9800, 0,9843, 0,9881 and 0,9990 respectively; while for the second training-test data set, they were 0,9920, 0,9939, 0,9891, 0,9828 and 0,9991; respectively. Within the scope of the study, finally, all the images were combined and the training and testing processes were repeated for a total of 556 X-ray images comprising 150 Covid-19 images and 406 non-Covid-19 images, by applying 2-fold cross. In this context, the average highest values of sensitivity, specificity, accuracy, F-1 score, and AUC for this last training-test data set were found to be 0,9760, 1,0000, 0,9906, 0,9823 and 0,9997; respectively.
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34
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Zhong X, Wang DL, Mo LF, Zhang W, Xiao LH, Wu XL, Chen YW, Yang L. Evaluation of the quality of COVID-19 prevention and control by a novel comprehensive evaluation model in a tertiary general hospital: a prospective observational study. BMC Public Health 2021; 21:2022. [PMID: 34742268 PMCID: PMC8571898 DOI: 10.1186/s12889-021-12032-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 10/15/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Prevention and control (P&C) of Corona Virus Disease 2019 (COVID-19) is still a critical task in most countries and regions. However, there are many single evaluation indexes to assess the quality of COVID-19 P&C. It is necessary to synthesize the single evaluation indexes reasonably to obtain the overall evaluation results. METHODS This study was divided into three steps. Step 1: In February 2020, the improved Delphi method was used to establish the quality evaluation indexes system for COVID-19 P&C. Step 2: in March 2020, the CRITIC method was used to adjust the Order Relation Analysis (G1) method to obtain the subjective and objective (S&O) combination weights. The comprehensive evaluation value was obtained using the weighted Efficacy Coefficient (EC) method, weighted TOPSIS method, weighted rank-sum ratio (RSR) method, and weighted Grey Relationship Analysis (GRA) method. Finally, the linear normalization method was used to synthesize the evaluation values of different evaluation methods. Step 3: From April 2020 to May 2021, this evaluation method was used to monitor and assess COVID-19 P&C quality in critical departments prospectively. The results were reported to the departments monthly. RESULT A quality evaluation indexes system for COVID-19 P&C was established. Kendall's consistency test shows that the four evaluation method had good consistency (χ2 = 43.429, P<0.001, Kendall's consistency coefficient = 0.835). The Spearman correlation test showed that the correlation between the combined evaluation results and the original method was statistically significant(P < 0.001). According to the Mann-Kendall test, from March 2020 to May 2021, the mean value of COVID-19 P&C quality in all critical departments showed an upward trend (P < 0.01). CONCLUSIONS The combined comprehensive evaluation method based on the S&O combined weight was more scientific and comprehensive than the single weighting and evaluation methods. In addition, monitoring and feedback of COVID-19 P&C quality were helpful for the improvement of P&C quality.
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Affiliation(s)
- Xiao Zhong
- Tertiary-grade & A-class office, Shenzhen Hospital, University of Chinese Academy of Sciences, Shenzhen, 518106, Guangdong, China.
| | - Dong-Li Wang
- Testing center, Guangming District Center for Disease Control and Prevention, Shenzhen, Guangdong, China
| | - Lan-Fang Mo
- Tertiary-grade & A-class office, Shenzhen Hospital, University of Chinese Academy of Sciences, Shenzhen, 518106, Guangdong, China
| | - Wen Zhang
- Tertiary-grade & A-class office, Shenzhen Hospital, University of Chinese Academy of Sciences, Shenzhen, 518106, Guangdong, China
| | - Li-Hua Xiao
- Tertiary-grade & A-class office, Shenzhen Hospital, University of Chinese Academy of Sciences, Shenzhen, 518106, Guangdong, China
| | - Xiang-Lin Wu
- Tertiary-grade & A-class office, Shenzhen Hospital, University of Chinese Academy of Sciences, Shenzhen, 518106, Guangdong, China
| | - Yan-Wei Chen
- Tertiary-grade & A-class office, Shenzhen Hospital, University of Chinese Academy of Sciences, Shenzhen, 518106, Guangdong, China
| | - Lei Yang
- Tertiary-grade & A-class office, Shenzhen Hospital, University of Chinese Academy of Sciences, Shenzhen, 518106, Guangdong, China
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35
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Mousavi Mojab SZ, Shams S, Fotouhi F, Soltanian-Zadeh H. EpistoNet: an ensemble of Epistocracy-optimized mixture of experts for detecting COVID-19 on chest X-ray images. Sci Rep 2021; 11:21564. [PMID: 34732741 PMCID: PMC8566470 DOI: 10.1038/s41598-021-00524-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 09/24/2021] [Indexed: 01/29/2023] Open
Abstract
The Coronavirus has spread across the world and infected millions of people, causing devastating damage to the public health and global economies. To mitigate the impact of the coronavirus a reliable, fast, and accurate diagnostic system should be promptly implemented. In this study, we propose EpistoNet, a decision tree-based ensemble model using two mixtures of discriminative experts to classify COVID-19 lung infection from chest X-ray images. To optimize the architecture and hyper-parameters of the designed neural networks, we employed Epistocracy algorithm, a recently proposed hyper-heuristic evolutionary method. Using 2500 chest X-ray images consisting of 1250 COVID-19 and 1250 non-COVID-19 cases, we left out 500 images for testing and partitioned the remaining 2000 images into 5 different clusters using K-means clustering algorithm. We trained multiple deep convolutional neural networks on each cluster to help build a mixture of strong discriminative experts from the top-performing models supervised by a gating network. The final ensemble model obtained 95% accuracy on COVID-19 images and 93% accuracy on non-COVID-19. The experimental results show that EpistoNet can accurately, and reliably be used to detect COVID-19 infection in the chest X-ray images, and Epistocracy algorithm can be effectively used to optimize the hyper-parameters of the proposed models.
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Affiliation(s)
- Seyed Ziae Mousavi Mojab
- Department of Computer Science, College of Engineering, Wayne State University, Detroit, MI, USA.
| | - Seyedmohammad Shams
- Medical Image Analysis Lab, Department of Radiology, Henry Ford Health System, Detroit, MI, USA
| | - Farshad Fotouhi
- Department of Computer Science, College of Engineering, Wayne State University, Detroit, MI, USA
| | - Hamid Soltanian-Zadeh
- Medical Image Analysis Lab, Department of Radiology, Henry Ford Health System, Detroit, MI, USA
- CIPCE, Department of ECE, College of Engineering, University of Tehran, Tehran, Iran
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36
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Abdulkareem KH, Mohammed MA, Salim A, Arif M, Geman O, Gupta D, Khanna A. Realizing an Effective COVID-19 Diagnosis System Based on Machine Learning and IoT in Smart Hospital Environment. IEEE INTERNET OF THINGS JOURNAL 2021; 8:15919-15928. [PMID: 35782183 PMCID: PMC8769008 DOI: 10.1109/jiot.2021.3050775] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/05/2020] [Accepted: 01/06/2021] [Indexed: 05/18/2023]
Abstract
The aim of this study is to propose a model based on machine learning (ML) and Internet of Things (IoT) to diagnose patients with COVID-19 in smart hospitals. In this sense, it was emphasized that by the representation for the role of ML models and IoT relevant technologies in smart hospital environment. The accuracy rate of diagnosis (classification) based on laboratory findings can be improved via light ML models. Three ML models, namely, naive Bayes (NB), Random Forest (RF), and support vector machine (SVM), were trained and tested on the basis of laboratory datasets. Three main methodological scenarios of COVID-19 diagnoses, such as diagnoses based on original and normalized datasets and those based on feature selection, were presented. Compared with benchmark studies, our proposed SVM model obtained the most substantial diagnosis performance (up to 95%). The proposed model based on ML and IoT can be served as a clinical decision support system. Furthermore, the outcomes could reduce the workload for doctors, tackle the issue of patient overcrowding, and reduce mortality rate during the COVID-19 pandemic.
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Affiliation(s)
| | - Mazin Abed Mohammed
- College of Computer Science and Information TechnologyUniversity of Anbar Anbar 00964 Iraq
| | - Ahmad Salim
- Department of Computer SystemsTechnical Institute of Anbar, Middle Technical University Baghdad 10074 Iraq
| | - Muhammad Arif
- School of Computer ScienceGuangzhou University Guangzhou 510006 China
| | - Oana Geman
- Department of Health and Human DevelopmentUniversitatea Stefan cel Mare din Suceava 720229 Suceava Romania
| | - Deepak Gupta
- Department of Computer Science and EngineeringMaharaja Agrasen Institute of Technology New Delhi 110086 India
| | - Ashish Khanna
- Department of Computer Science and EngineeringMaharaja Agrasen Institute of Technology New Delhi 110086 India
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Mutlag AA, Abd Ghani MK, Mohammed MA, Lakhan A, Mohd O, Abdulkareem KH, Garcia-Zapirain B. Multi-Agent Systems in Fog-Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring. SENSORS (BASEL, SWITZERLAND) 2021; 21:6923. [PMID: 34696135 PMCID: PMC8537170 DOI: 10.3390/s21206923] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/07/2021] [Accepted: 10/09/2021] [Indexed: 12/17/2022]
Abstract
In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%.
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Affiliation(s)
- Ammar Awad Mutlag
- Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Malaysia; (A.A.M.); (M.K.A.G.); (O.M.)
- Ministry of Education/General Directorate of Curricula, Pure Science Department, Baghdad 10065, Iraq
| | - Mohd Khanapi Abd Ghani
- Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Malaysia; (A.A.M.); (M.K.A.G.); (O.M.)
| | - Mazin Abed Mohammed
- College of Computer Science and Information Technology, University of Anbar, 11, Ramadi 31001, Iraq
| | - Abdullah Lakhan
- Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China;
| | - Othman Mohd
- Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Malaysia; (A.A.M.); (M.K.A.G.); (O.M.)
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Canayaz M. C+EffxNet: A novel hybrid approach for COVID-19 diagnosis on CT images based on CBAM and EfficientNet. CHAOS, SOLITONS, AND FRACTALS 2021; 151:111310. [PMID: 34376926 PMCID: PMC8339545 DOI: 10.1016/j.chaos.2021.111310] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 07/14/2021] [Accepted: 07/28/2021] [Indexed: 05/03/2023]
Abstract
COVID-19, one of the biggest diseases of our age, continues to spread rapidly around the world. Studies continue rapidly for the diagnosis and treatment of this disease. It is of great importance that individuals who are infected with this virus be isolated from the rest of the society so that the disease does not spread further. In addition to the tests performed in the detection process of the patients, X-ray and computed tomography are also used. In this study, a new hybrid model that can diagnose COVID-19 from computed tomography images created using EfficientNet, one of the current deep learning models, with a model consisting of attention blocks is proposed. In the first step of this new model, channel attention, spatial attention, and residual blocks are used to extract the most important features from the images. The extracted features are combined in accordance with the hyper-column technique. The combined features are given as input to the EfficientNet models in the second step of the model. The deep features obtained from this proposed hybrid model were classified with the Support Vector Machine classifier after feature selection. Principal Components Analysis was used for feature selection. The approach can accurately predict COVID-19 with a 99% accuracy rate. The first four versions of EfficientNet are used in the approach. In addition, Bayesian optimization was used in the hyper parameter estimation of the Support Vector Machine classifier. Comparative performance analysis of the approach with other approaches in the field is given.
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Affiliation(s)
- Murat Canayaz
- Department of Computer Engineering,Van Yuzuncu Yil University,65100,Van,Turkey
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Razzaq OA, Fahad M, Khan NA. Different variants of pandemic and prevention strategies: A prioritizing framework in fuzzy environment. RESULTS IN PHYSICS 2021; 28:104564. [PMID: 34307006 PMCID: PMC8286550 DOI: 10.1016/j.rinp.2021.104564] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 06/27/2021] [Accepted: 07/13/2021] [Indexed: 06/13/2023]
Abstract
In this trying time for the world battling different variants of the COVID'19 pandemic, different intervention strategies are being taken by government, to limit the spread of infection. Closing educational institutes, stay at home orders, campaigns for emphasis on vaccination, usage of medical mask and frequently sanitizing hands, etc. are the endeavors made by the authorities to decrease the number of cases in the country. In this regard, the contribution aims to help the decision-makers to identify a potential prevention strategy, based on public acceptance and intervention effectiveness. To achieve this objective, feasible judgments of professionals from three different sectors are brought together through meetings. Opinions, based on ten criteria, are recorded in linguistic form for prioritizing six alternatives. The linguistic terms are then evaluated and manipulated by entailing triangular fuzzy numbers and a group multi-criteria decision making (GMCDM) approach. After using the fuzzy analytical hierarchy process (F-AHP) for the complex decisions, the fuzzy VIšekriterijumsko KOmpromisno Rangiranje method (F-VIKOR) is utilized to attain the closest ideal stratagem. Consequently, through the ranking orders of defuzzified scores, intuitive preference of compromise solutions is suggested. The tactic gaining more priority with respect to the group utility to the majority and F-VIKOR index is complete lockdown for the short term. Furthermore, a comparison analysis is also added in the discussion to verify the attained prioritized outcomes. This comparative study is carried out through the technique for order of preference by similarity to ideal solution (TOPSIS), which evidently produces the same preference of alternatives. In addition, this strategy can be apparently discovered to be an effective strategy adopted by different countries in successfully decreasing the number of cases.
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Affiliation(s)
- Oyoon Abdul Razzaq
- Department of Humanities & Social Sciences, Bahria Humanities and Social Sciences School, Bahria University, Karachi 75260, Pakistan
| | - Muhammad Fahad
- Department of Mathematics, University of Karachi, Karachi 75270, Pakistan
| | - Najeeb Alam Khan
- Department of Mathematics, University of Karachi, Karachi 75270, Pakistan
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COVID-CGAN: Efficient Deep Learning Approach for COVID-19 Detection Based on CXR Images Using Conditional GANs. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11167174] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
COVID-19, a novel coronavirus infectious disease, has spread around the world, resulting in a large number of deaths. Due to a lack of physicians, emergency facilities, and equipment, medical systems have been unable to treat all patients in many countries. Deep learning is a promising approach for providing solutions to COVID-19 based on patients’ medical images. As COVID-19 is a new disease, its related dataset is still being collected and published. Small COVID-19 datasets may not be sufficient to build powerful deep learning detection models. Such models are often over-fitted, and their prediction results cannot be generalized. To fill this gap, we propose a deep learning approach for accurately detecting COVID-19 cases based on chest X-ray (CXR) images. For the proposed approach, named COVID-CGAN, we first generated a larger dataset using generative adversarial networks (GANs). Specifically, a customized conditional GAN (CGAN) was designed to generate the target COVID-19 CXR images. The expanded dataset, which contains 84.8% generated images and 15.2% original images, was then used for training five deep detection models: InceptionResNetV2, Xception, SqueezeNet, VGG16, and AlexNet. The results show that the use of the synthetic CXR images, which were generated by the customized CGAN, helped all deep learning models to achieve high detection accuracies. In particular, the highest accuracy was achieved by the InceptionResNetV2 model, which was 99.72% accurate with only ten epochs. All five models achieved kappa coefficients between 0.81 and 1, which is interpreted as an almost perfect agreement between the actual labels and the detected labels. Furthermore, the experiment showed that some models were faster yet smaller compared to the others but could still achieve high accuracy. For instance, SqueezeNet, which is a small network, required only three minutes and achieved comparable accuracy to larger networks such as InceptionResNetV2, which needed about 143 min. Our proposed approach can be applied to other fields with scarce datasets.
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AFCM-LSMA: New intelligent model based on Lévy slime mould algorithm and adaptive fuzzy C-means for identification of COVID-19 infection from chest X-ray images. ADVANCED ENGINEERING INFORMATICS 2021; 49. [PMCID: PMC8126092 DOI: 10.1016/j.aei.2021.101317] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Problem A worldwide challenge is to provide medical resources required for COVID-19 detection. They must be effective tools for fast detection and diagnose of the virus using a large number of tests; besides, they should be low-cost developments. While a chest X-ray scan is a powerful candidate tool, if several tests are carried out, the images produced by the devices must be interpreted accurately and rapidly. COVID-19 induces longitudinal pulmonary parenchymal ground-glass and consolidates pulmonary opacity, in some cases with rounded morphology and peripheral lung distribution, which is very difficult to predict in an early stage. Aim In this paper, we aim to develop a robust model to extract high-level features of COVID-19 from chest X-ray (CXR) images to help in rapid diagnosis. In specific, this paper proposes an optimization model for COVID-19 diagnosis based on adaptive Fuzzy C-means (AFCM) and improved Slime Mould Algorithm (SMA) based on Lévy distribution, namely AFCM-LSMA. Methods The SMA optimizer is proposed to adapt weights in oscillation mode and to mimic the process of generating positive and negative feedback from the propagation wave to shape the optimum path for food connectivity. Lévy motion is used as a permutation to perform a local search and to adapt SMA optimizer (LSMA) by generating several solutions that are apart from current candidates. Furthermore, it permits the optimizer to escape from local minima, examine large search areas and reach optimal solutions in fewer iterations with high convergence speed. The FCM algorithm is used to segment pulmonary regions from CXR images and is adapted to reduce time and amount of computations using histogram of the image intensities during the clustering process. Results The performance of the proposed AFCM-LSMA has been validated on CXR images and compared with different conventional machine learning and deep learning techniques, meta-heuristics methods, and different chaotic maps. The accuracies achieved by the proposed model are around (ACC = 0.96, RMSE = 0.23, Prec. = 0.98, F1_score = 0.98, MCC = 0.79, and Kappa = 0.79). Conclusion The experimental findings indicate that the proposed new method outperforms all other methods, which will be beneficial to the clinical practitioner for the early identification of infected COVID-19 patients.
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Khozeimeh F, Sharifrazi D, Izadi NH, Joloudari JH, Shoeibi A, Alizadehsani R, Gorriz JM, Hussain S, Sani ZA, Moosaei H, Khosravi A, Nahavandi S, Islam SMS. Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Sci Rep 2021; 11:15343. [PMID: 34321491 PMCID: PMC8319175 DOI: 10.1038/s41598-021-93543-8] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/25/2021] [Indexed: 02/07/2023] Open
Abstract
COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have shown outstanding classification performance on image datasets. To date, it appears that COVID computer-aided diagnosis systems based on CNNs and clinical information have not yet been analysed or explored. We propose a novel method, named the CNN-AE, to predict the survival chance of COVID-19 patients using a CNN trained with clinical information. Notably, the required resources to prepare CT images are expensive and limited compared to those required to collect clinical data, such as blood pressure, liver disease, etc. We evaluated our method using a publicly available clinical dataset that we collected. The dataset properties were carefully analysed to extract important features and compute the correlations of features. A data augmentation procedure based on autoencoders (AEs) was proposed to balance the dataset. The experimental results revealed that the average accuracy of the CNN-AE (96.05%) was higher than that of the CNN (92.49%). To demonstrate the generality of our augmentation method, we trained some existing mortality risk prediction methods on our dataset (with and without data augmentation) and compared their performances. We also evaluated our method using another dataset for further generality verification. To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.
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Affiliation(s)
- Fahime Khozeimeh
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Danial Sharifrazi
- Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
| | - Navid Hoseini Izadi
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran
| | | | - Afshin Shoeibi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
- Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia.
| | - Juan M Gorriz
- Department of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Spain
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Sadiq Hussain
- System Administrator, Dibrugarh University, Assam, 786004, India
| | | | - Hossein Moosaei
- Department of Mathematics, Faculty of Science, University of Bojnord, Bojnord, Iran
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, VIC 3216, Australia
| | - Sheikh Mohammed Shariful Islam
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, VIC, 3220, Australia
- Cardiovascular Division, The George Institute for Global Health, Newtown, Australia
- Sydney Medical School, University of Sydney, Camperdown, Australia
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Kamal Pasha M, Gardazi SFA, Imtiaz F, Qureshi AT, Afrasiab R. Identification of efficient COVID-19 diagnostic test through artificial neural networks approach − substantiated by modeling and simulation. JOURNAL OF INTELLIGENT SYSTEMS 2021. [DOI: 10.1515/jisys-2021-0041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Soon after the first COVID-19 positive case was detected in Wuhan, China, the virus spread around the globe, and in no time, it was declared as a global pandemic by the WHO. Testing, which is the first step in identifying and diagnosing COVID-19, became the first need of the masses. Therefore, testing kits for COVID-19 were manufactured for efficiently detecting COVID-19. However, due to limited resources in the densely populated countries, testing capacity even after a year is still a limiting factor for COVID-19 diagnosis on a larger scale and contributes to a lag in disease tracking and containment. Due to this reason, we started this study to provide a better cost-effective solution for enhancing the testing capacity so that the maximum number of people could get tested for COVID-19. For this purpose, we utilized the approach of artificial neural networks (ANN) to acquire the relevant data on COVID-19 and its testing. The data were analyzed by using Machine Learning, and probabilistic algorithms were applied to obtain a statistically proven solution for COVID-19 testing. The results obtained through ANN indicated that sample pooling is not only an effective way but also regarded as a “Gold standard” for testing samples when the prevalence of the disease is low in the population and the chances of getting a positive result are less. We further demonstrated through algorithms that pooling samples from 16 individuals is better than pooling samples of 8 individuals when there is a high likelihood of getting negative test results. These findings provide ground to the fact that if sample pooling will be employed on a larger scale, testing capacity will be considerably increased within limited available resources without compromising the test specificity. It will provide healthcare units and enterprises with solutions through scientifically proven algorithms, thus, saving a considerable amount of time and finances. This will eventually help in containing the spread of the pandemic in densely populated areas including vulnerably confined groups, such as nursing homes, hospitals, cruise ships, and military ships.
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Affiliation(s)
| | - Syed Fasih Ali Gardazi
- School of Electrical Engineering and Computer Science, National University of Sciences and Technology , Islamabad , Pakistan
| | - Fariha Imtiaz
- Punjab University College of Pharmacy, University of the Punjab, Allama Iqbal Campus , Lahore , Pakistan
| | - Asma Talib Qureshi
- Atta Ur Rahman School of Applied Biosciences, National University of Sciences and Technology , Islamabad , Pakistan
| | - Rabia Afrasiab
- Department of Medicine, Unit 2, University of Health Sciences , Lahore , Pakistan
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Jayachitra VP, Nivetha S, Nivetha R, Harini R. A cognitive IoT-based framework for effective diagnosis of COVID-19 using multimodal data. Biomed Signal Process Control 2021; 70:102960. [PMID: 34249142 PMCID: PMC8260502 DOI: 10.1016/j.bspc.2021.102960] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 06/22/2021] [Accepted: 07/01/2021] [Indexed: 11/24/2022]
Abstract
The COVID-19 emerged at the end of 2019 and has become a global pandemic. There are many methods for COVID-19 prediction using a single modality. However, none of them predicts with 100% accuracy, as each individual exhibits varied symptoms for the disease. To decrease the rate of misdiagnosis, multiple modalities can be used for prediction. Besides, there is also a need for a self-diagnosis system to narrow down the risk of virus spread in testing centres. Therefore, we propose a robust IoT and deep learning-based multi-modal data classification method for the accurate prediction of COVID-19. Generally, highly accurate models require deep architectures. In this work, we introduce two lightweight models, namely CovParaNet for audio (cough, speech, breathing) classification and CovTinyNet for image (X-rays, CT scans) classification. These two models were identified as the best unimodal models after comparative analysis with the existing benchmark models. Finally, the obtained results of the five independently trained unimodal models are integrated by a novel dynamic multimodal Random Forest classifier. The lightweight CovParaNet and CovTinyNet models attain a maximum accuracy of 97.45% and 99.19% respectively even with a small dataset. The proposed dynamic multimodal fusion model predicts the final result with 100% accuracy, precision, and recall, and the online retraining mechanism enables it to extend its support even in a noisy environment. Furthermore, the computational complexity of all the unimodal models is minimized tremendously and the system functions effectively with 100% reliability even in the absence of any one of the input modalities during testing.
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Affiliation(s)
- V P Jayachitra
- Department of Computer Technology, MIT campus, Anna University, Chennai, India
| | - S Nivetha
- Department of Computer Technology, MIT campus, Anna University, Chennai, India
| | - R Nivetha
- Department of Computer Technology, MIT campus, Anna University, Chennai, India
| | - R Harini
- Department of Computer Technology, MIT campus, Anna University, Chennai, India
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Dogan O, Tiwari S, Jabbar MA, Guggari S. A systematic review on AI/ML approaches against COVID-19 outbreak. COMPLEX INTELL SYST 2021; 7:2655-2678. [PMID: 34777970 PMCID: PMC8256231 DOI: 10.1007/s40747-021-00424-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 06/05/2021] [Indexed: 12/24/2022]
Abstract
A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.
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Affiliation(s)
- Onur Dogan
- Department of Industrial Engineering, Izmir Bakircay University, 35665 Izmir, Turkey.,Research Center for Data Analytics and Spatial Data Modeling (RC-DAS), Izmir Bakircay University, 35665 Izmir, Turkey
| | - Sanju Tiwari
- Department of Computer Science, Universidad Autonoma de Tamaulipas, Ciudad Victoria, Mexico
| | - M A Jabbar
- Vardhaman College of Engineering, Kacharam, India
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Alkan N, Kahraman C. Evaluation of government strategies against COVID-19 pandemic using q-rung orthopair fuzzy TOPSIS method. Appl Soft Comput 2021; 110:107653. [PMID: 34226821 PMCID: PMC8241659 DOI: 10.1016/j.asoc.2021.107653] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 05/12/2021] [Accepted: 06/22/2021] [Indexed: 12/14/2022]
Abstract
The COVID-19 outbreak, which emerged in China and continues to spread rapidly all over the world, has brought with it increasing numbers of cases and deaths. Governments have suffered serious damage and losses not only in the field of health but also in many other fields. This has directed governments to adopt and implement various strategies in their communities. However, only a few countries succeed partially from the strategies implemented while other countries have failed. In this context, it is necessary to identify the most important strategy that should be implemented by governments. A decision problem based on the decisions of many experts, with some contradictory and multiple criteria, should be taken into account in order to evaluate the multiple strategies implemented by various governments. In this study, this decision process is considered as a multi-criteria decision making (MCDM) problem that also takes into account uncertainty. For this purpose, q-rung orthopair fuzzy sets (q-ROFSs) are used to allow decision-makers to their assessments in a wider space and to better deal with ambiguous information. Accordingly, two different Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches are recommended under the q-ROFS environment and applied to determine the most appropriate strategy. The results of the proposed approaches determine the A1 — Mandatory quarantine and strict isolation strategy as the best strategy. Comparisons with other q-rung orthopair fuzzy MCDM methods and intuitionistic fuzzy TOPSIS method are also presented for the validation of the proposed methods. Besides, sensitivity analyses are conducted to check the robustness of the proposed approaches and to observe the effect of the change in the q parameter.
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Affiliation(s)
- Nurşah Alkan
- Istanbul Technical University, Industrial Engineering Department, 34367 Macka, Istanbul, Turkey
| | - Cengiz Kahraman
- Istanbul Technical University, Industrial Engineering Department, 34367 Macka, Istanbul, Turkey
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Luo J, Zhou L, Feng Y, Li B, Guo S. The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity. PLoS One 2021; 16:e0253329. [PMID: 34129653 PMCID: PMC8208037 DOI: 10.1371/journal.pone.0253329] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 06/03/2021] [Indexed: 02/05/2023] Open
Abstract
The global pandemic of COVID-19 poses a huge threat to the health and lives of people all over the world, and brings unprecedented pressure to the medical system. We need to establish a practical method to improve the efficiency of treatment and optimize the allocation of medical resources. Due to the influx of a large number of patients into the hospital and the running of medical resources, blood routine test became the only possible check while COVID-19 patients first go to a fever clinic in a community hospital. This study aims to establish an efficient method to identify key indicators from initial blood routine test results for COVID-19 severity prediction. We determined that age is a key indicator for severity predicting of COVID-19, with an accuracy of 0.77 and an AUC of 0.92. In order to improve the accuracy of prediction, we proposed a Multi Criteria Decision Making (MCDM) algorithm, which combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Naïve Bayes (NB) classifier, to further select effective indicators from patients' initial blood test results. The MCDM algorithm selected 3 dominant feature subsets: {Age, WBC, LYMC, NEUT} with a selection rate of 44%, {Age, NEUT, LYMC} with a selection rate of 38%, and {Age, WBC, LYMC} with a selection rate of 9%. Using these feature subsets, the optimized prediction model could achieve an accuracy of 0.82 and an AUC of 0.93. These results indicated that Age, WBC, LYMC, NEUT were the key factors for COVID-19 severity prediction. Using age and the indicators selected by the MCDM algorithm from initial blood routine test results can effectively predict the severity of COVID-19. Our research could not only help medical workers identify patients with severe COVID-19 at an early stage, but also help doctors understand the pathogenesis of COVID-19 through key indicators.
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Affiliation(s)
- Jiaqing Luo
- School of Computer Science and Engineering, University of Electronic
Science and Technology of China, Chengdu, China
| | - Lingyun Zhou
- Center of Infectious Diseases, West China Hospital of Sichuan University,
Chengdu, China
| | - Yunyu Feng
- State Key Laboratory of Biotherapy and Cancer Center, West China
Hospital, Sichuan University and Collaborative Innovation Center, Chengdu,
China
| | - Bo Li
- Department of Otorhinolaryngology, Head & Neck Surgery, West China
Hospital, Sichuan University, Chengdu, China
| | - Shujin Guo
- The Geriatric Respiratory Department, Sichuan Provincial People’s
Hospital, University of Electronic Science and Technology of China, Chengdu,
China
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48
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Madhavan MV, Khamparia A, Gupta D, Pande S, Tiwari P, Hossain MS. Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning. Neural Comput Appl 2021; 35:13907-13920. [PMID: 34127892 PMCID: PMC8188748 DOI: 10.1007/s00521-021-06171-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 05/25/2021] [Indexed: 12/31/2022]
Abstract
Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. There are high chances of getting false positives and false negatives identifying the COVID-19 symptoms through existing medical practices such as PCR (polymerase chain reaction) and RT-PCR (reverse transcription-polymerase chain reaction). It might lead to a community spread of the disease. The alternative of these tests can be CT (Computer Tomography) imaging or X-rays of the lungs to identify the patient with COVID-19 symptoms more accurately. Furthermore, by using feasible and usable technology to automate the identification of COVID-19, the facilities can be improved. This notion became the basic framework, Res-CovNet, of the implemented methodology, a hybrid methodology to bring different platforms into a single platform. This basic framework is incorporated into IoMT based framework, a web-based service to identify and classify various forms of pneumonia or COVID-19 utilizing chest X-ray images. For the front end, the.NET framework along with C# language was utilized, MongoDB was utilized for the storage aspect, Res-CovNet was utilized for the processing aspect. Deep learning combined with the notion forms a comprehensive implementation of the framework, Res-CovNet, to classify the COVID-19 affected patients from pneumonia-affected patients as both lung imaging looks similar to the naked eye. The implemented framework, Res-CovNet, developed with the technique, transfer learning in which ResNet-50 used as a pre-trained model and then extended with classification layers. The work implemented using the data of X-ray images collected from the various trustable sources that include cases such as normal, bacterial pneumonia, viral pneumonia, and COVID-19, with the overall size of the data is about 5856. The accuracy of the model implemented is about 98.4% in identifying COVID-19 against the normal cases. The accuracy of the model is about 96.2% in the case of identifying COVID-19 against all other cases, as mentioned.
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Affiliation(s)
- Mangena Venu Madhavan
- School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab India
| | - Aditya Khamparia
- Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Amethi, India
| | - Deepak Gupta
- Maharaja Agrasen Institute of Technology, Rohini, India
| | - Sagar Pande
- School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab India
| | - Prayag Tiwari
- Department of Computer Science, Aalto University, Espoo, Finland
| | - M. Shamim Hossain
- Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, 11543 Saudi Arabia
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49
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Singh G, Yow KC. An Interpretable Deep Learning Model for Covid-19 Detection With Chest X-Ray Images. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:85198-85208. [PMID: 35256923 PMCID: PMC8864958 DOI: 10.1109/access.2021.3087583] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 06/05/2021] [Indexed: 05/28/2023]
Abstract
Timely and accurate detection of an epidemic/pandemic is always desired to prevent its spread. For the detection of any disease, there can be more than one approach including deep learning models. However, transparency/interpretability of the reasoning process of a deep learning model related to health science is a necessity. Thus, we introduce an interpretable deep learning model: Gen-ProtoPNet. Gen-ProtoPNet is closely related to two interpretable deep learning models: ProtoPNet and NP-ProtoPNet The latter two models use prototypes of spacial dimension [Formula: see text] and the distance function [Formula: see text]. In our model, we use a generalized version of the distance function [Formula: see text] that enables us to use prototypes of any type of spacial dimensions, that is, square spacial dimensions and rectangular spacial dimensions to classify an input image. The accuracy and precision that our model receives is on par with the best performing non-interpretable deep learning models when we tested the models on the dataset of [Formula: see text]-ray images. Our model attains the highest accuracy of 87.27% on classification of three classes of images, that is close to the accuracy of 88.42% attained by a non-interpretable model on the classification of the given dataset.
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Affiliation(s)
- Gurmail Singh
- Faculty of Engineering and Applied SciencesUniversity of ReginaReginaSKS4S 0A2Canada
| | - Kin-Choong Yow
- Faculty of Engineering and Applied SciencesUniversity of ReginaReginaSKS4S 0A2Canada
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50
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Serena Low WC, Chuah JH, Tee CATH, Anis S, Shoaib MA, Faisal A, Khalil A, Lai KW. An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:5528144. [PMID: 34194535 PMCID: PMC8184329 DOI: 10.1155/2021/5528144] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/19/2021] [Accepted: 05/19/2021] [Indexed: 12/15/2022]
Abstract
Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant's technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.
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Affiliation(s)
- Woan Ching Serena Low
- Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 40603 Kuala Lumpur, Malaysia
| | - Joon Huang Chuah
- Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 40603 Kuala Lumpur, Malaysia
| | - Clarence Augustine T. H. Tee
- Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 40603 Kuala Lumpur, Malaysia
| | - Shazia Anis
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 40603 Kuala Lumpur, Malaysia
| | - Muhammad Ali Shoaib
- Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 40603 Kuala Lumpur, Malaysia
| | - Amir Faisal
- Department of Biomedical Engineering, Faculty of Production and Industrial Technology, Institut Teknologi Sumatera, Lampung 35365, Indonesia
| | - Azira Khalil
- Faculty of Science and Technology, Universiti Sains Islam Malaysia, 71800 Nilai, Negeri Sembilan, Malaysia
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 40603 Kuala Lumpur, Malaysia
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