Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images.

Autor: Sharifrazi D; Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran., Alizadehsani R; Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia., Roshanzamir M; Department of Computer Engineering, Faculty of Engineering, Fasa University, 74617-81189, Fasa, Iran., Joloudari JH; Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran., Shoeibi A; 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., Jafari M; Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran., Hussain S; System Administrator, Dibrugarh University, Assam, 786004, India., Sani ZA; Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.; Omid Hospital, Iran University of Medical Sciences, Tehran, Iran., Hasanzadeh F; Omid Hospital, Iran University of Medical Sciences, Tehran, Iran., Khozeimeh F; Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia., Khosravi A; Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia., Nahavandi S; Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia., Panahiazar M; Institute for Computational Health Sciences, University of California, San Francisco, USA., Zare A; Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran., Islam SMS; Institute for Physical Activity and Nutrition, Deakin University, Melbourne, Australia.; Cardiovascular Division, The George Institute for Global Health, Australia.; Sydney Medical School, University of Sydney, Australia., Acharya UR; Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore.; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan.
Jazyk: angličtina
Zdroj: Biomedical signal processing and control [Biomed Signal Process Control] 2021 Jul; Vol. 68, pp. 102622. Date of Electronic Publication: 2021 Apr 08.
DOI: 10.1016/j.bspc.2021.102622
Abstrakt: The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filter (CNN-SVM + Sobel) achieved the highest classification accuracy, sensitivity and specificity of 99.02%, 100% and 95.23%, respectively in automated detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application.
Competing Interests: The authors have no competing interests to declare.
(© 2021 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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