ISSN: 0973-7510

E-ISSN: 2581-690X

Research Article | Open Access
Madhumita Pal1,2, Smita Parija1 , Ganapati Panda1, Snehasish Mishra3,Ranjan K. Mohapatra4 and Kuldeep Dhama5
1Department of Electronics and communication Engineering, CV Raman Global University, Bidyanagar, Mahura, Janla, Bhubaneswar, Odisha, India.
2Department of Electrical Engineering, Government College of Engineering, Keonjhar, Odisha, India.
3School of Biotechnology, Campus-11, KIIT Deemed University, Bhubaneswar, Odisha, India.
4Department of Chemistry, Government College of Engineering, Keonjhar, Odisha, India.
5Division of Pathology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, India.
Article Number: 8413 | © The Author(s). 2023
J Pure Appl Microbiol. 2023;17(2):919-930. https://doi.org/10.22207/JPAM.17.2.20
Received: 08 January 2023 | Accepted: 01 February 2023 | Published online: 04 May 2023
Issue online: June 2023
Abstract

Global public health is overwhelmed due to the ongoing Corona Virus Disease (COVID-19). As of October 2022, the causative virus SARS-CoV-2 and its multiple variants have infected more than 600 million confirmed cases and nearly 6.5 million fatalities globally. The main objective of this reported study is to understand the COVID-19 infection better from the chest X-ray (CXR) image database of COVID-19 cases from the dataset of CXR of normal, pneumonia and COVID-19 patients. Deep learning approaches like VGG-16 and LSTM models were used to classify images as normal, pneumonia and COVID-19 impacted by extracting the features. It has been observed during the COVID-19 pandemic peaks that large number of patients could not avail medical beds and were seen stranded outdoors. To address such health emergency situations with limited available bed and scarcity of expert physicians, computer-aided analysis could save precious lives through early screening and appropriate care. Such computer-based deep-learning strategy could help during future pandemics, especially when the available health resources and the need for preventive measures to take do not match the burden of a disease.

Keywords

COVID-19 Prognosis, X-ray Image, Deep Learning, Long Short-term Memory (LSTM), VGG 16

Article Metrics

Article View: 368

Share This Article

© The Author(s) 2023. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License which permits unrestricted use, sharing, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.