Authors : Dr. G. A. V. Ramachandra Rao; Kallepalli Mythri; Kintali Meghana; Korada Karthik; Manepalli Bobby

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/mtr5uf7p

Scribd : https://tinyurl.com/trfzdsus

DOI : https://doi.org/10.38124/ijisrt/26May1510

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Early detection of lung-related disorders is critical in providing timely treatment; however, detecting any minor anomalies in the lung region from the chest X-ray image is difficult, taking much time to process manually. As a result, it is required to have an automatic system that can analyze chest X-ray images. For this research, deep learning technol- ogy is employed in analyzing chest X-ray images, where the chosen deep learning model is called EfficientNetB0, capable of extracting important patterns and categorizing them as normal, lung Opacity, or viral pneumonia. In order to enhance the performance of the deep learning model, the chest X-ray images are preprocessed using Contrast Limited Adaptive Histogram Equalization (CLAHE). Once the images are preprocessed, the system produces predictions based on the category and provides an accuracy measure. It is also responsible for assigning a risk level, alongside highlighting important areas of the images using Grad-CAM.

Keywords : Deep Learning, Chest X-ray, Lung Disease Classification, EfficientNetB0, CLAHE, Grad-CAM, Medical Image Analysis.

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