AI-Powered Pneumonia Detection Using Deep Learning on Chest X-Ray Images

Abstract This document presents a detailed case study on using convolutional neural networks (CNNs) to detect pneumonia with artificial intelligence. Pneumonia remains a major global health threat, making quick and accurate diagnosis crucial for reducing death rates and improving patient care. Traditional diagnostic methods often take a lot of time and require specialists, which can lead to delays and mistakes in treatment decisions. In this study, we introduce an automated system that analyzes chest X-ray images using deep learning techniques to quickly identify pneumonia. The model used a publicly available dataset for training and was evaluated with common performance metrics, such as accuracy, precision, recall, and F1- score. The results show how AI can help healthcare professionals with quick diagnosis and better decision-making. The research also covers the development of a web-based platform for real-time predictions, improving access for real-world use in medical environments. The study details the methodology, findings, and importance of this approach, as well as future research directions aimed at improving the model's effectiveness and expanding its application. Keywords: Pneumonia Detection, Deep Learning, Convolutional Neural Networks, Chest X-Ray, Medical Imaging, AI in Healthcare