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
Publication history: 
Received on 15 November 2022; revised on 25 December 2022; accepted on 28 December 2022
 
Abstract: 
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.
 
Keywords: 
Coronavirus (COVID-19); CNN; Radiological images; Classification
 
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