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ISSN Approved Journal || eISSN: 2582-8185 || CODEN: IJSRO2 || Impact Factor 8.2 || Google Scholar and CrossRef Indexed

Peer Reviewed and Referred Journal || Free Certificate of Publication

Research and review articles are invited for publication in March 2026 (Volume 18, Issue 3) Submit manuscript

Diagnosis of COVID-19 cases on X-Ray images using CNN

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  • Diagnosis of COVID-19 cases on X-Ray images using CNN

Amulya Nampally *, Sanjana Koyyada, Sri Vaishnavi and Meeravali Shaik

Department of Computer Science and Engineering, SNIST, Hyderabad-501301, India.

Research Article
International Journal of Science and Research Archive, 2023, 08(01), 031-037.
Article DOI: 10.30574/ijsra.2023.8.1.0338
DOI url: https://doi.org/10.30574/ijsra.2023.8.1.0338

Received on 15 November 2022; revised on 25 December 2022; accepted on 28 December 2022

COVID-19 is a viral disease that has killed more than 10 million people worldwide and infected millions of people. Therefore, it has become necessary to screen large numbers of people to detect infected individuals and reduce the spread of the disease. Maximum spread for confirming a virus is estimated with RT-PCR test. PCR (Polymerize Chain Response) is a popular device for predicting pathological examination. A key issue with real-time RT-PCR testing is the risk of generating false-negative and false-positive results. As an adjunct to RT-PCR, Computed Tomography (CT) can be used to diagnose COVID-19. In this article, using CXR scans, we proposed a deep-layered convolutional neural network (CNN) for accurate COVID-19 detection. Our model yields 97% accuracy.

Coronavirus (COVID-19); CNN; Radiological images; Classification

https://ijsra.net/sites/default/files/fulltext_pdf/IJSRA-2022-0338.pdf

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Amulya Nampally, Sanjana Koyyada, Sri Vaishnavi and Meeravali Shaik. Diagnosis of COVID-19 cases on X-Ray images using CNN. International Journal of Science and Research Archive, 2023, 08(01), 031-037. Article DOI: https://doi.org/10.30574/ijsra.2023.8.1.0338

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


All statements, opinions, and data contained in this publication are solely those of the individual author(s) and contributor(s). The journal, editors, reviewers, and publisher disclaim any responsibility or liability for the content, including accuracy, completeness, or any consequences arising from its use.

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