Open Access

ARTICLE

Deep Convolutional Neural Network Approach for COVID-19 Detection

by Yu Xue^1,2,*, Bernard-Marie Onzo¹, Romany F. Mansour^3,4, Shoubao Su⁴

1 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China
3 Department of Mathematics, Faculty of Science, New Valley University, El-Kharja, 72511, Egypt
4 Jiangsu Key Laboratory of Data Science and Smart Software, Jinling Institute of Technology, Nanjing, 211169, China

* Corresponding Author: Yu Xue. Email:

Computer Systems Science and Engineering 2022, 42(1), 201-211. https://doi.org/10.32604/csse.2022.022158

Received 29 July 2021; Accepted 24 September 2021; Issue published 02 December 2021

Abstract

Coronavirus disease 2019 (Covid-19) is a life-threatening infectious disease caused by a newly discovered strain of the coronaviruses. As by the end of 2020, Covid-19 is still not fully understood, but like other similar viruses, the main mode of transmission or spread is believed to be through droplets from coughs and sneezes of infected persons. The accurate detection of Covid-19 cases poses some questions to scientists and physicians. The two main kinds of tests available for Covid-19 are viral tests, which tells you whether you are currently infected and antibody test, which tells if you had been infected previously. Routine Covid-19 test can take up to 2 days to complete; in reducing chances of false negative results, serial testing is used. Medical image processing by means of using Chest X-ray images and Computed Tomography (CT) can help radiologists detect the virus. This imaging approach can detect certain characteristic changes in the lung associated with Covid-19. In this paper, a deep learning model or technique based on the Convolutional Neural Network is proposed to improve the accuracy and precisely detect Covid-19 from Chest Xray scans by identifying structural abnormalities in scans or X-ray images. The entire model proposed is categorized into three stages: dataset, data pre-processing and final stage being training and classification.

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