Published in

IGI Global, International Journal of E-Health and Medical Communications, 2(13), p. 1-13, 2021

DOI: 10.4018/ijehmc.20220701.oa4

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Binary Classification of COVID-19 CT Images Using CNN

Journal article published in 2022 by Shankar Shambhu ORCID, Deepika Koundal ORCID, Prasenjit Das, Chetan Sharma ORCID
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

Full text: Unavailable

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Data provided by SHERPA/RoMEO

Abstract

COVID-19 pandemic has hit the world with such a force that the world's leading economies are finding it challenging to come out of it. Countries with the best medical facilities are even cannot handle the increasing number of cases and fatalities. This disease causes significant damage to the lungs and respiratory system of humans, leading to their death. Computed tomography (CT) images of the respiratory system are analyzed in the proposed work to classify the infected people with non-infected people. Deep learning binary classification algorithms have been applied, which have shown an accuracy of 86.9% on 746 CT images of chest having COVID-19 related symptoms.