A Deep Learning Model with Transfer Learning and Attention for Accurate Pneumonia Detection in Chest X-Rays

Abstract

Pneumonia is a serious lung infection that needs quick and correct diagnosis to prevent health complications and deaths. Traditional methods of diagnosis take time and may sometimes lead to mistakes, which makes computer-based medical support systems highly useful. In this study, we introduce a deep learning model that automatically detects and classifies pneumonia from chest X-ray images. The model uses a combination of convolutional neural networks (CNNs) with an attention layer to focus on important image details, while transfer learning helps in using knowledge from existing medical datasets for better performance. A feature fusion method is also applied to combine different types of image features, which improves the accuracy of the results. Tests carried out on publicly available datasets show that the proposed model provides higher accuracy, sensitivity, and specificity compared to several existing techniques. This research demonstrates how advanced deep learning methods can assist doctors in diagnosing pneumonia earlier, reduce errors, and support faster clinical decisions.