Pneumonia Detector: Development of a Deep Learning-Based Pneumonia Detection System Using Chest X-ray Images

Pneumonia is a critical respiratory infection that can lead to severe health complications if not diagnosed and treated in time. In this project, we propose a deep learning-based approach for the automatic detection of pneumonia from chest X-ray images using Convolutional Neural Networks (CNNs). The proposed system leverages the Xception architecture as a feature extractor combined with a custom classification head, data preprocessing, augmentation, and Grad-CAM visualization to localize the affected regions. The model was trained and evaluated on a publicly available chest X-ray dataset, with performance metrics including accuracy, precision, recall, and F1-score used to assess its effectiveness.The final implementation was done in a modular Python environment using Google Colab, allowing convenient integration of data pipelines, training workflows, and visualization techniques. This project demonstrates the potential of deep learning in automating pneumonia diagnosis and providing assistive tools for clinical decision-making