CAPTCHA
12 + 6 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
  • Reset your password

User account menu

  • Log in
Home
Open Scholar Sphere

Main navigation

  • Home

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

Breadcrumb

  • Home

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/node/1002

Preview Article PDF

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. https://doi.org/10.30574/ijsra.2023.8.1.0338

Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0

Footer menu

  • Contact
Powered by Drupal

Copyright © 2026 Open Scholar Sphere - All rights reserved

Developed & Designed by VS WebTech