AI Based Pneumonia Detection System using Transfer Learning and Web Integration

Early and correct identification of pneumonia is essential for decreasing death rates and enhancing patient outcomes, particularly in healthcare environments with limited resources. This study introduces an advanced Pneumonia Detection System that utilizes sophisticated transfer learning algorithms to categorize chest X-ray pictures into two distinct classes: normal and pneumonia-infected. The method gets excellent classification accuracy by using pre-trained deep learning models like VGG16, ResNet50, or InceptionV3 and applying learnt features from big datasets to the particular job of finding lung infections. The backend of the system is built using Python and TensorFlow/Keras, which makes it easy to train and test models on medical pictures. The server-side application is built using a lightweight Flask web framework, which makes it easy for the deep learning model and the user interface to operate together. HTML and CSS are used to build the front end. This makes it easy to submit chest X-ray photos and get prediction results right away. Healthcare practitioners may use the system without needing to know a lot about technology thanks to this connection. This method helps doctors make quicker diagnoses and lessens the workload on radiologists by automating the process of finding pneumonia. It also gives a scalable base for adding more diagnostic features in the future, which will help AI-driven healthcare solutions become better. The suggested system is a potential tool for early pneumonia screening in both clinical and remote settings since it has a user-friendly interface and a strong deep learning architecture.