Binary Classification of COVID-19 CT Images Using CNN: COVID Diagnosis Using CT

Shankar Shambhu, Deepika Koundal, Prasenjit Das and Chetan Sharma
Additional contact information
Shankar Shambhu: Chitkara University School of Computer Applications, Chitkara University, Himachal Pradesh, India
Deepika Koundal: Department of Vitualization, School of Computer Science, Universit of Petroleum and Energy Studies, Dehradun, India
Prasenjit Das: Chitkara University School of Computer Applications, Chitkara University, Himachal Pradesh, India
Chetan Sharma: Chitkara University, Himachal Pradesh, India

International Journal of E-Health and Medical Communications (IJEHMC), 2021, vol. 13, issue 2, 1-13

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.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... /IJEHMC.20220701.oa4 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jehmc0:v:13:y:2021:i:2:p:1-13

Access Statistics for this article

International Journal of E-Health and Medical Communications (IJEHMC) is currently edited by Joel J.P.C. Rodrigues

More articles in International Journal of E-Health and Medical Communications (IJEHMC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().