Open Access

ARTICLE

An Automated Classification Technique for COVID-19 Using Optimized Deep Learning Features

by Ejaz Khan¹, Muhammad Zia Ur Rehman², Fawad Ahmed³, Suliman A. Alsuhibany^4,*, Muhammad Zulfiqar Ali⁵, Jawad Ahmad⁶

1 School of Engineering, RMIT University, Melbourne, Australia
2 Department of Electrical Engineering, HITEC University Taxila, 47080, Pakistan
3 Department of Cyber Security, Pakistan Navy Engineering College, NUST, Karachi 75350, Pakistan
4 Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
5 James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK
6 School of Computing, Edinburgh Napier University, Edinburgh, EH10 5DT, UK

* Corresponding Author: Suliman A. Alsuhibany. Email:

Computer Systems Science and Engineering 2023, 46(3), 3799-3814. https://doi.org/10.32604/csse.2023.037131

Received 25 October 2022; Accepted 02 February 2023; Issue published 03 April 2023

Abstract

In 2020, COVID-19 started spreading throughout the world. This deadly infection was identified as a virus that may affect the lungs and, in severe cases, could be the cause of death. The polymerase chain reaction (PCR) test is commonly used to detect this virus through the nasal passage or throat. However, the PCR test exposes health workers to this deadly virus. To limit human exposure while detecting COVID-19, image processing techniques using deep learning have been successfully applied. In this paper, a strategy based on deep learning is employed to classify the COVID-19 virus. To extract features, two deep learning models have been used, the DenseNet201 and the SqueezeNet. Transfer learning is used in feature extraction, and models are fine-tuned. A publicly available computerized tomography (CT) scan dataset has been used in this study. The extracted features from the deep learning models are optimized using the Ant Colony Optimization algorithm. The proposed technique is validated through multiple evaluation parameters. Several classifiers have been employed to classify the optimized features. The cubic support vector machine (Cubic SVM) classifier shows superiority over other commonly used classifiers and attained an accuracy of 98.72%. The proposed technique achieves state-of-the-art accuracy, a sensitivity of 98.80%, and a specificity of 96.64%.

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Keywords

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