Advanced Pneumonia Detection and Severity Analysis Using CLAHE-CNN and GRAD-CAM

Abstract

The Pneumonia is a serious respiratory infection that can lead to severe health complications and increased mortality if not diagnosed and treated at an early stage. Conventional diagnosis using chest X-ray imaging is time-consuming and highly dependent on the expertise of radiologists, which may not always be readily available in healthcare facilities. To address this challenge, this project presents an Advanced Pneumonia Detection and Severity Analysis System from Chest X-Ray Images Using CLAHE-Enhanced Convolutional Neural Networks (CNN) with Grad-CAM Visualization and Clinical Recommendation Support. The proposed system utilizes Contrast Limited Adaptive Histogram Equalization (CLAHE) as a preprocessing technique to enhance the quality and contrast of chest X-ray images, thereby improving feature extraction and classification performance. A deep CNN model is trained on labeled chest radiographs to automatically classify images as Pneumonia or Normal while also assessing the severity level of infection. The model learns discriminative patterns from enhanced X-ray images and provides accurate predictions with improved robustness. To improve transparency and interpretability, Gradientweighted Class Activation Mapping (Grad-CAM) is integrated into the framework to generate heatmap visualizations that highlight the infected lung regions responsible for the model’s predictions. The performance of the proposed model is evaluated using metricssuch as accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results demonstrate that the integration of CLAHE enhancement and Grad-CAM interpretability significantly improves diagnostic reliability and model understanding. The developed system offers an efficient, accurate, and explainable AI-assisted solution for early pneumonia diagnosis, severity assessment, timely treatment support, and improvement