Deep learning for COVID-19 diagnosis based on chest X-ray images

Nashat Alrefai, Othman Ibrahim


Coronavirus disease 2019 (COVID-19) is a recent global pandemic that has affected many countries around the world, causing serious health problems, especially in the lungs. Although temperature testing is suggested as a first-line test for COVID-19, it was not reliable because many diseases have the same symptoms. Thus, we propose a deep learning method based on X-ray images that used a convolutional neural network (CNN) and transfer learning (TL) for COVID-19 diagnosis, and using gradient-weighted class activation mapping (Grad-CAM) technique for producing visual explanations for the COVID-19 infection area in the lung. The low sample size of coronavirus samples was considered a challenge, thus, this issue was overridden using data augmentation techniques. The study found that the proposed (CNN) and the modified pre-trained networks VGG16 and InceptionV3 achieved a promising result for COVID-19 diagnosis by using chest X-ray images. The proposed CNN was able to differentiate 284 patients with COVID-19 or normal with 98.2 percent for training accuracy and 96.66 percent for test accuracy and 100.0 percent sensitivity. The modified VGG16 achieved the best classification result between all with 100.0 percent for training accuracy and 98.33 percent for test accuracy and 100.0 percent sensitivity, but the proposed CNN overcame the others in the side of reducing the computational complexity and training time significantly.


chest X-ray; convolutional neural network; COVID-19; deep learning; transfer learning;


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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578