A hybrid approach for COVID-19 detection using biogeography-based optimization and deep learning

Venkatachalam, K. and Siuly, Siuly and Kumar, M. Vinoth and Lalwani, Praveen and Mishra, Manas and Kabir, Enamul ORCID: https://orcid.org/0000-0002-6157-2753 (2021) A hybrid approach for COVID-19 detection using biogeography-based optimization and deep learning. Computers, Materials and Continua, 70 (2). pp. 3717-3732. ISSN 1546-2218

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Abstract

The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care services. An early diagnosis of COVID-19 may reduce the impact of the coronavirus. To achieve this objective, modern computation methods, such as deep learning, may be applied. In this study, a computational model involving deep learning and biogeography-based optimization (BBO) for early detection and management of COVID-19 is introduced. Specifically, BBO is used for the layer selection process in the proposed convolutional neural network (CNN). The computational model accepts images, such as CT scans, X-rays, positron emission tomography, lung ultrasound, and magnetic resonance imaging, as inputs. In the comparative analysis, the proposed deep learning model CNN is compared with other existing models, namely, VGG16, InceptionV3, ResNet50, and MobileNet. In the fitness function formation, classification accuracy is considered to enhance the prediction capability of the proposed model. Experimental results demonstrate that the proposed model outperforms InceptionV3 and ResNet50.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: 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.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Date Deposited: 06 Oct 2021 23:42
Last Modified: 08 Nov 2021 01:38
Uncontrolled Keywords: Covid-19; biogeography-based optimization; deep learning; convolutional neural network; computer vision
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Socio-Economic Objectives (2020): 22 INFORMATION AND COMMUNICATION SERVICES > 2299 Other information and communication services > 229999 Other information and communication services not elsewhere classified
Identification Number or DOI: doi:10.32604/cmc.2022.018487
URI: http://eprints.usq.edu.au/id/eprint/43752

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