Accurate Diagnosis of COVID-19 Based on Deep Neural Networks and Chest X-Ray Images

Moustafa, Hossam El-Din; El-Seddek, Mervat

doi:10.21608/bfemu.2020.30471.1001

Accurate Diagnosis of COVID-19 Based on Deep Neural Networks and Chest X-Ray Images

Document Type : Review articles

Authors

¹ Mansoura University Faculty of Engineering Electronics and Communications Department

² Department of Electronics and Communications - Misr Higher Institute for Engineering and Technology - Mansoura

10.21608/bfemu.2020.30471.1001

Abstract

The present study aims at preventing spread out of COVID-19 by early detection of infected cases using chest X-ray images and convolutional neural networks. Covid-19 chest X-ray dataset were collected from public sources as well as through agreements with hospitals and physicians with the consent of their patients. A deep learning algorithm based on convolutional neural networks (CNN) was implemented utilizing X-ray images to diagnose COVID-19. ResNet50, short for Residual Networks, is a classic neural network that was used as a backbone for the classification task. It accelerates the speed of training of the deep networks and reduces the effect of vanishing gradient problems. Images were first resized and then pre-processed to increase sharpness, contrast, and clarity. Images were fed into a deep neural network to predict the probability of COVID-19 infectious. The deep learning calculation acquired an area under the curve (AUC) of the receiver operating characteristics (ROC) of 0.9888, 96.2% sensitivity, 98% accuracy, and 100% specificity. Moreover, the algorithm can be easily modified to add extra images (normal and COVID-19) to improve performance. The proposed system introduces a great help to all nations to screen and diagnose COVID-19 as a faster alternative compared with conventional method that uses PCR.

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