Convolutional neural network for the detection of coronavirus based on X-ray images

Abstract

Nowadays, <span lang="EN-US">the coronavirus disease (COVID-19) is considered an ongoing pandemic that spread quickly in most countries around the world. The COVID-19 causes severe acute respiratory syndrome. Moreover, the technique of chest computed tomography (CT) is a method used in the detection of COVID-19. However, the CT method consumes more time and higher-cost as compared with chest X-ray images. Therefore, this paper presents convolutional neural network (CNN) algorithm in the detection of COVID-19 by using X-ray images. In this method, we have used a balanced image database for the normal (healthy) and COVID-19 subjects. The total number of image database is 188 samples (94 healthy samples and 94 COVID-19 samples). Furthermore, there are several evaluation measurements are used to evaluate the proposed model such as accuracy, precision, specificity, sensitivity, F-measure, G-mean, and others. According to the experimental results, the proposed model obtains 98.68% accuracy, 100% precision, and 100% specificity. Besides, the proposed model achieves 97.37%, 98.67%, and 98.68% for sensitivity, F-measure, and G-mean, respectively. The performance of the proposed model by using CNN algorithm shows promising results in the detection of COVID-19. Also, it has outperformed all its comparatives in terms of detection accuracy.</span>

Detecting COVID-19 in chest X-ray images

“…The model was composed of CNN as the feature extraction and long short-term memory (LSTM) for classifying chest x-ray images into three classes: normal, COVID-19, and pneumonia. In [12], the proposed method consisted of a 15-layer CNN architecture. The model was trained on 112 chest x-ray images which consisted of 56 normal images and 56 COVID-19 images.…”