@Article{cmc.2023.032192,
AUTHOR = {Mahmoud Ragab, Mohammed W. Al-Rabia, Sami Saeed Binyamin, Ahmed A. Aldarmahi},
TITLE = {Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {74},
YEAR = {2023},
NUMBER = {2},
PAGES = {2889--2903},
URL = {http://www.techscience.com/cmc/v74n2/50240},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2023.032192}
}