Deep Learning Framework for Pneumonia Identification Using Convolutional Neural Networks

Pneumonia is still a major public health issue, particularly in areas where there is limited access to early medical diagnosis. This project puts forward an AI-based solution that uses deep learning methods to identify pneumonia automatically from chest X-ray. A Convolutional Neural Network (CNN) model is constructed and trained to identify healthy lungs and pneumonia-infected lungs with high precision. The trained model is interfaced with a web-based application developed using the Django framework to facilitate users in interacting with the system through an easy-to-use interface. With the capability to upload images rapidly and obtain diagnosis results instantly, the system aids early intervention and also helps healthcare professionals in making decisions. This paper points to the possibility of integrating machine learning with web technologies to build easy-to-access, efficient, and scalable healthcare tools. As interest in artificial intelligence for medical use continues to grow, deep learning models have worked exceptionally well in image classification, particularly in medical imaging. Chest X-rays, which are frequently applied as a diagnostic tool, provide useful visual patterns that can be effectively examined by machine learning models. Yet, manual interpretation is time-consuming and could result in variability in diagnosis. With this automation, the suggested system is looking to reduce human error and expedite diagnosis, particularly in regions with a shortage of medical professionals.