Abstract
Pneumonia is a serious respiratory infection that poses significant health risks, particularly if not diagnosed and treated promptly. Traditional methods of pneumonia diagnosis rely on the manual interpretation of chest X-ray images by radiologists, a process that can be time-consuming, subjective, and error-prone, especially in regions with limited access to experienced medical professionals. To address these challenges, this study explores the development of an automated deep learning-based system for pneumonia detection using chest X-ray images. The results demonstrate that the deep learning model can achieve high levels of accuracy, sensitivity, and specificity, making it a valuable tool for assisting radiologists in diagnosing pneumonia more quickly and reliably. Moreover, the system's scalability and ease of deployment make it particularly beneficial in resource-limited settings, where timely and accurate diagnosis is crucial. This research highlights the potential of deep learning to revolutionize medical diagnostics, improving patient outcomes through enhanced diagnostic accuracy and efficiency.