Open Access

ARTICLE

Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

Mahmoud Ragab^1,2,3,*, Mohammed W. Al-Rabia^4,5, Sami Saeed Binyamin⁶, Ahmed A. Aldarmahi^7,8

1 Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
2 Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia
3 Mathematics Department, Faculty of Science, Al-Azhar University, Naser City 11884, Cairo, Egypt
4 Department of Medical Microbiology and Parasitology, Faculty of Medicine, King Abdulaziz University, Jeddah 21589, Saudi Arabia
5 Health Promotion Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
6 Computer and Information Technology Department, The Applied College, King Abdulaziz University, Jeddah 21589, Saudi Arabia
7 Basic Science Department, College of Science and Health Professions, King Saud Bin Abdulaziz University for Health Sciences, Jeddah 21423, Saudi Arabia
8 King Abdullah International Medical Research Center, Ministry of National Guard-Health Affairs, Jeddah 21423, Saudi Arabia

* Corresponding Author: Mahmoud Ragab. Email:

Computers, Materials & Continua 2023, 74(2), 2889-2903. https://doi.org/10.32604/cmc.2023.032192

Received 10 May 2022; Accepted 10 June 2022; Issue published 31 October 2022

Abstract

With the increasing and rapid growth rate of COVID-19 cases, the healthcare scheme of several developed countries have reached the point of collapse. An important and critical steps in fighting against COVID-19 is powerful screening of diseased patients, in such a way that positive patient can be treated and isolated. A chest radiology image-based diagnosis scheme might have several benefits over traditional approach. The accomplishment of artificial intelligence (AI) based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition systems. This study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System (IFFA-DTLMS). The proposed IFFA-DTLMS model majorly aims at identifying and categorizing the occurrence of COVID19 on chest radiographs. To attain this, the presented IFFA-DTLMS model primarily applies densely connected networks (DenseNet121) model to generate a collection of feature vectors. In addition, the firefly algorithm (FFA) is applied for the hyper parameter optimization of DenseNet121 model. Moreover, autoencoder-long short term memory (AE-LSTM) model is exploited for the classification and identification of COVID19. For ensuring the enhanced performance of the IFFA-DTLMS model, a wide-ranging experiments were performed and the results are reviewed under distinctive aspects. The experimental value reports the betterment of IFFA-DTLMS model over recent approaches.

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Keywords

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