Deep Learning-Based Chest X-Ray Diagnostic Assistant with Grad-CAM Visual Explanations

Objective: To develop and deploy an AI-powered system for automated chest X-ray disease detection with interpretable Grad-CAM visual explanations to assist radiologists in Indian healthcare settings.

Chest diseases such as pneumonia, tuberculosis, and COVID-19 pose significant health burdens across India, where radiologist shortages and high patient volumes in public hospitals lead to delays and diagnostic errors, especially in rural and tier-2/3 cities.

This project proposes an end-to-end machine learning solution: a web-based assistant that analyzes chest X-ray images using deep convolutional neural networks, highlights pathological regions using Grad-CAM for interpretability, and presents findings in a user-friendly dashboard for clinicians.

Key deliverables include: (1) a trained and validated model deployed via an interactive web or desktop interface, (2) Grad-CAM heatmaps to explain predictions, (3) support for multiple chest conditions, (4) integration with open-source Indian chest X-ray datasets, and (5) a cost analysis and deployment on affordable hardware for practical adoption.

This solution can augment radiology workflows, reduce diagnostic errors, enhance trust in AI via transparency, and scale to resource-limited settings, with potential for integration into India’s public health infrastructure and telemedicine platforms.