Open Access

ARTICLE

A Hybrid Approach for COVID-19 Detection Using Biogeography-Based Optimization and Deep Learning

K. Venkatachalam¹, Siuly Siuly², M. Vinoth Kumar³, Praveen Lalwani¹, Manas Kumar Mishra¹, Enamul Kabir^4,*

1 Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India
2 Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, 14428, Australia
3 Department of Computer Science and Engineering, Anna University, University College of Engineering, Dindigul, 624622, India
4 School of Sciences, University of Southern Queensland, Toowoomba, Darling Heights, 4350, Australia

* Corresponding Author: Enamul Kabir. Email:

Computers, Materials & Continua 2022, 70(2), 3717-3732. https://doi.org/10.32604/cmc.2022.018487

Received 10 March 2021; Accepted 19 April 2021; Issue published 27 September 2021

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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