relation: https://khub.utp.edu.my/scholars/17700/
title: COVID Detection Using Chest X-Ray and Transfer Learning
creator: Jain, S.
creator: Sindhwani, N.
creator: Anand, R.
creator: Kannan, R.
description: As per World Health Organization, COVID-19 is causing even the most important health systems across the countries under considerable strain. The advanced recognition of COVID 19 will result into decreasing the stress of a lot of health systems. Much similar to the customary usage of Chest X-Rays for detecting different pathologies, COVID-19 can also be detected using X-Ray of patients that indicates a very critical function in the diagnosis of SARS Covid-19. With rampant growth in the area of Deep Learning (DL) as well as Machine Learning (ML), it is much easier to design the framework that can detect COVID-19 infection easily. This paper proposes deep learning-based detection process by incorporating the concept of Transfer Learning for the classification of this pandemic using X-ray images of chest. This non-invasive and early-prediction of the corona virus by observing the X-rays of chest can subsequently be utilized to estimate the expansion of COVID-19 in the patients. This study got a maximum of 97 classifiers� accuracy using ResNet based model. This method can be utilized to upscale the effectiveness of the screening process. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2022
type: Article
type: PeerReviewed
identifier:   Jain, S. and Sindhwani, N. and Anand, R. and Kannan, R.  (2022) COVID Detection Using Chest X-Ray and Transfer Learning.  Lecture Notes in Networks and Systems, 418 LN.  pp. 933-943.  ISSN 23673370     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127672464&doi=10.1007%2f978-3-030-96308-8_87&partnerID=40&md5=00f52e3cc1bf92f1756740b882e2905e
relation: 10.1007/978-3-030-96308-8₈₇
identifier: 10.1007/978-3-030-96308-8₈₇