Open Access

ARTICLE

A Deep Learning Approach for Detecting Covid-19 Using the Chest X-Ray Images

Fatemeh Sadeghi¹, Omid Rostami², Myung-Kyu Yi³, Seong Oun Hwang^3,*

1 Department of Industrial Engineering, Sharif University of Technology, Tehran, 14588-89694, Iran
2 Department of Industrial Engineering, University of Houston, Houston, TX, 77204, USA
3 Department of Computer Engineering, Gachon University, Seongnam, 13120, Korea

* Corresponding Author: Seong Oun Hwang. Email:

Computers, Materials & Continua 2023, 74(1), 751-768. https://doi.org/10.32604/cmc.2023.031519

Received 20 April 2022; Accepted 24 June 2022; Issue published 22 September 2022

Abstract

Real-time detection of Covid-19 has definitely been the most widely-used world-wide classification problem since the start of the pandemic from 2020 until now. In the meantime, airspace opacities spreads related to lung have been of the most challenging problems in this area. A common approach to do on that score has been using chest X-ray images to better diagnose positive Covid-19 cases. Similar to most other classification problems, machine learning-based approaches have been the first/most-used candidates in this application. Many schemes based on machine/deep learning have been proposed in recent years though increasing the performance and accuracy of the system has still remained an open issue. In this paper, we develop a novel deep learning architecture to better classify the Covid-19 X-ray images. To do so, we first propose a novel multi-habitat migration artificial bee colony (MHMABC) algorithm to improve the exploitation/exploration of artificial bee colony (ABC) algorithm. After that, we optimally train the fully connected by using the proposed MHMABC algorithm to obtain better accuracy and convergence rate while reducing the execution cost. Our experiment results on Covid-19 X-ray image dataset show that the proposed deep architecture has a great performance in different important optimization parameters. Furthermore, it will be shown that the MHMABC algorithm outperforms the state-of-the-art algorithms by evaluating its performance using some well-known benchmark datasets.

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Keywords

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