Pneumonia Identification based on Transfer Learning over Chest X-Ray Images

Pneumonia remains a significant health concern globally, necessitating rapid and accurate detection methods. This study proposes an advanced pneumonia detection framework leveraging transfer learning with deep learning models, specifically ResNet50, VGG16, and MobileNetV3, for chest X-ray image classification. By integrating the strengths of ResNet and MobileNetV3 with attention mechanisms, the model effectively addresses common challenges like data asymmetry and subtle disease patterns. The model was trained on a publicly available Kaggle dataset consisting of 5,863 X-ray images categorized as normal and pneumonia. The proposed transfer learning approach achieved a remarkable training accuracy of 95.94% and a test accuracy of 88.87%, outperforming traditional models. This research demonstrates the model’s capability to accurately classify pneumonia with reduced training time and computational overhead, presenting a reliable and scalable solution for clinical applications.