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IJEETC 2023 Vol.12(6): 424-432
doi: 10.18178/ijeetc.12.6.424-432

Detection of SARS-COVID-19 Based on CT Images Using Deep Learning-Based Hybrid Particle Swarm and Mayfly Optimization Algorithm

Manish K. Assudani* and Neeraj Sahu
Raisoni Centre for Research and Innovation, G. H. Raisoni University, Amravati, India

Manuscript received May 7, 2023; revised July 10, 2023;; accepted September 2, 2023.

Abstract—More than 25 million people worldwide have contracted COVID-19 due to the SARS-COV-2 disease. Screening many suspected cases for quarantine and treatment is crucial to controlling the disease. Pathogenic laboratory testing is the gold standard but has a high false-negative rate, making alternate diagnostic procedures necessary to fight the disease. Based on COVID-19 radiographic changes in Computed Tomography (CT) scans, this research hypothesized that artificial intelligence (AI) approaches may extract particular graphical features and offer a clinical diagnosis before the pathogenic test, saving time for disease management. 1065 CT images of pathogen-confirmed COVID-19 and typical viral pneumonia patients were obtained. To develop the method, we proposed Deep Learning (DL)-based hybrid Particle Swarm Optimization and Mayfly Optimization (PSO-MFO) algorithm. PSO-MFO as a classifier to detect SARS-COVID-19. Internal validation yielded accuracy, specificity, and sensitivity. The external testing dataset has accuracy, specificity, and sensitivity. These findings show that AI can extract radiological features for COVID-19 diagnosis.

Index Terms—COVID-19, Particle Swarm Optimization (PSO), Mayfly Optimization (MFO), CT scan

Cite: Manish K. Assudani and Neeraj Sahu, "Detection of SARS-COVID-19 Based on CT Images Using Deep Learning-Based Hybrid Particle Swarm and Mayfly Optimization Algorithm," International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 12, No. 6, pp. 424-432, November 2023. doi: 10.18178/ijeetc.12.6.424-432

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.