Sharifrazi, D. and Alizadehsani, R. and Roshanzamir, M. and Joloudari, J.H. and Shoeibi, A. and Jafari, M. and Hussain, S. and Sani, Z.A. and Hasanzadeh, F. and Khozeimeh, F. and Khosravi, A. and Nahavandi, S. and Panahiazar, M. and Zare, A. and Islam, S.M.S. and Acharya, U.R. (2021) Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images. Biomedical Signal Processing and Control, 68.
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Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images.pdf Download (9MB) | Preview |
Abstract
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. © 2021 Elsevier Ltd
| Item Type: | Article |
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| Additional Information: | cited By 2 |
| Uncontrolled Keywords: | Computerized tomography; Convolution; Convolutional neural networks; Data mining; Deep learning; Feature extraction; Filtration; High pass filters, Convolutional neural network; Covid-19; Deep learning; Features extraction; Images processing; Machine-learning; Network support; Sobel filter; Sobel operator; Support vectors machine, Support vector machines, adult; Article; artificial intelligence; binary classification; comparative study; computer assisted diagnosis; controlled study; convolutional neural network; coronavirus disease 2019; cross validation; deep learning; diagnostic test accuracy study; early diagnosis; edge detection; false negative result; human; major clinical study; middle aged; prevalence; priority journal; sensitivity and specificity; support vector machine; thorax radiography |
| Subjects: | WC Communicable Diseases |
| Depositing User: | eprints admin |
| Date Deposited: | 01 Nov 2021 08:24 |
| Last Modified: | 01 Nov 2021 08:51 |
| URI: | http://eprints.iums.ac.ir/id/eprint/39162 |
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