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International Journal of Advanced Research in Computer and Communication Engineering A monthly Peer-reviewed & Refereed journal
ISSN Online 2278-1021ISSN Print 2319-5940Since 2012
IJARCCE adheres to the suggestive parameters outlined by the University Grants Commission (UGC) for peer-reviewed journals, upholding high standards of research quality, ethical publishing, and academic excellence.
← Back to VOLUME 14, ISSUE 7, JULY 2025

COVID-19 Chest X-ray Classification Web App

Dr. Kavyashree N, Chaithra S J

DOI: 10.17148/IJARCCE.2025.14708

Abstract: COVID-19's worldwide spread has put a strain on healthcare systems, especially in recognising it quickly. Despite its time and expense, RT-PCR is the most preferred testing technique. This web-based project classifies chest X-rays as COVID-19, Pneumonia, or Normal using deep learning. Clinicians can check for COVID-19 quicker. The project uses CNN models learned on free medical imaging datasets. This model can discriminate COVID-19 radiography indications among additional lung infections and healthy lungs. Web interfaces simplify chest X-ray submissions. After scanning the image, the backend system instantly categorises correctly. This programme is simple and effective. This helps radiologists, doctors, and researchers make decisions. It cannot substitute for clinical examinations, but it is useful when medical competence is lacking or early detection is important. The programme shows how AI may enhance public health response and digital diagnostics can avoid pandemics.

Keywords: COVID-19, Chest X-ray, Deep Learning, CNN (Convolutional Neural Network), Medical Imaging, Image Classification, Web Application, Healthcare AI, Pneumonia Detection, Diagnostic Tool.

How to Cite:

[1] Dr. Kavyashree N, Chaithra S J, “COVID-19 Chest X-ray Classification Web App,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.14708