yuvan-x/pneumonia-detection-with-explainable-ai

This project is an AI based classifier that analyzes chest X-ray images to detect pneumonia. It uses a CNN, short for Convolutional Neural Network, a type of AI architecture designed to recognize patterns in images, to classify whether an X-ray shows signs of pneumonia or looks normal. To make the AI's decision easier to trust, it also applies Grad-CAM, a technique that generates a heatmap over the X-ray highlighting which parts of the image the model focused on when making its prediction. This lets a viewer see which regions of the lung the model treated as abnormal, rather than just receiving a plain yes or no answer with no visual justification. The project is built in Python. The README for this repository is very brief, stating only the project's name and this one line description, so details such as installation steps, the training dataset used, model accuracy, or how to run a prediction are not available from the source. Anyone wanting to use this project would need to look at the code directly to understand its structure and requirements. This kind of project is a common learning exercise for people studying medical image classification and explainable AI techniques, pairing a standard CNN classifier with a visualization method that is widely used in that field. It would be most useful to a student or hobbyist exploring how Grad-CAM works on a real image classification task, rather than to someone looking for a ready to use diagnostic tool.