Open Access iconOpen Access

ARTICLE

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

Mahmoud Ragab1,2,3,*, Mohammed W. Al-Rabia4,5, Sami Saeed Binyamin6, Ahmed A. Aldarmahi7,8

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: email

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

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.

Keywords


Cite This Article

M. Ragab, M. W. Al-Rabia, S. S. Binyamin and A. A. Aldarmahi, "Intelligent firefly algorithm deep transfer learning based covid-19 monitoring system," Computers, Materials & Continua, vol. 74, no.2, pp. 2889–2903, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1016

    View

  • 407

    Download

  • 0

    Like

Share Link