Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images

Prabal Datta Barua, Nadia Fareeda Muhammad Gowdh, Kartini Rahmat, Norlisah Ramli, Wei Lin Ng, Wai Yee Chan, Mutlu Kuluozturk, Sengul Dogan, Mehmet Baygin, Orhan Yaman, Turker Tuncer, Tao Wen, Kang Hao Cheong and U. Rajendra Acharya
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Prabal Datta Barua: School of Management & Enterprise, University of Southern Queensland, Toowoomba 2550, Australia
Nadia Fareeda Muhammad Gowdh: Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia
Kartini Rahmat: Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia
Norlisah Ramli: Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia
Wei Lin Ng: Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia
Wai Yee Chan: Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia
Mutlu Kuluozturk: Department of Pulmonology Clinic, Firat University Hospital, Firat University, Elazig 23119, Turkey
Sengul Dogan: Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey
Mehmet Baygin: Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan 75000, Turkey
Orhan Yaman: Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey
Turker Tuncer: Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, Turkey
Tao Wen: Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, 8 Somapah Road, Singapore S485998, Singapore
Kang Hao Cheong: Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, 8 Somapah Road, Singapore S485998, Singapore
U. Rajendra Acharya: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore S599489, Singapore

IJERPH, 2021, vol. 18, issue 15, 1-20

Abstract: COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application.

Keywords: Exemplar COVID-19FclNet9; deep feature generation; transfer learning; COVID-19 detection; iterative NCA (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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