Pneumonia Detection Using Deep Learning on Chest Xray Images

Pneumonia is a serious lung infection that requires early diagnosis for effective treatment. Deep learning techniques have shown promising results in medical image analysis, particu- larly in chest X-ray classification. Previous research has utilized DenseNet-121 for pneumonia detection due to its efficient feature reuse capabilities. In this paper, we propose a deep learning-based approach using the TResNet model, which provides improved performance and computational efficiency. The model is trained using transfer learning on chest X-ray datasets and enhanced with preprocessing and data augmentation techniques. Experimental results demon- strate that the proposed TResNet model achieves better accuracy and robustness compared to traditional DenseNet-121 approaches. This system can assist healthcare professionals in faster and more reliable diagnosis of pneumonia.