Open Access

ARTICLE

Detection of Omicron Caused Pneumonia from Radiology Images Using Convolution Neural Network (CNN)

by Arfat Ahmad Khan¹, Malik Muhammad Ali Shahid², Rab Nawaz Bashir², Salman Iqbal², Arshad Shehzad Ahmad Shahid³, Javeria Maqbool⁴, Chitapong Wechtaisong^5,*

1 College of Computing, Khon Kaen Univerity, Khon Kaen, 40000, Thailand
2 Department of Computer Science, COMSATS University Islamabad, Vehari, 61100, Pakistan
3 Department of Petroleum & Gas Engineering, University of Engineering & Technology, 54890, Pakistan
4 Electrical Engineering Department, SBASSE, Lahore University of Management Sciences, Lahore, 54890, Pakistan
5 School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand

* Corresponding Author: Chitapong Wechtaisong. Email:

Computers, Materials & Continua 2023, 74(2), 3743-3761. https://doi.org/10.32604/cmc.2023.033924

Received 01 July 2022; Accepted 27 August 2022; Issue published 31 October 2022

Abstract

COVID-19 disease caused by the SARS-CoV-2 virus has created social and economic disruption across the world. The ability of the COVID-19 virus to quickly mutate and transfer has created serious concerns across the world. It is essential to detect COVID-19 infection caused by different variants to take preventive measures accordingly. The existing method of detection of infections caused by COVID-19 and its variants is costly and time-consuming. The impacts of the COVID-19 pandemic in developing countries are very drastic due to the unavailability of medical facilities and infrastructure to handle the pandemic. Pneumonia is the major symptom of COVID-19 infection. The radiology of the lungs in varies in the case of bacterial pneumonia as compared to COVID-19-caused pneumonia. The pattern of pneumonia in lungs in radiology images can also be used to identify the cause associated with pneumonia. In this paper, we propose the methodology of identifying the cause (either due to COVID-19 or other types of infections) of pneumonia from radiology images. Furthermore, because different variants of COVID-19 lead to different patterns of pneumonia, the proposed methodology identifies pneumonia, the COVID-19 caused pneumonia, and Omicron caused pneumonia from the radiology images. To fulfill the above-mentioned tasks, we have used three Convolution Neural Networks (CNNs) at each stage of the proposed methodology. The results unveil that the proposed step-by-step solution enhances the accuracy of pneumonia detection along with finding its cause, despite having a limited dataset.

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