COPD: Pneumonia and Pneumothorax Detection in Chest X-rays: Vision Transfer based on Deep Learning

Recently, there has been a growing interest in the application of deep learning for the automated analysis of chest X-rays (CXRs) especially for the detection of pneumothorax. Conventional deep learning models such as convolutional neural networks (CNNs) have shown to perform well in CXR classification. However, CNN-based methods are limited by the local dependence of the feature extraction in their inherent form, which prevents them from capturing long-range dependencies in medical images. Furthermore, CNNs have a domain shift problem, which limits their flexibility in varying imaging conditions and are typically black box models that are difficult to interpret and incorporate into clinical decision-making processes. In this paper, we designed a novel Vision Transfer (ViT) based framework for the classification of pneumonia and pneumothorax from CXRs. In contrast to CNNs, ViTs use self-attention to model global dependencies and are thus well-placed to detect diffuse opacities in pneumonia, and pleural abnormalities in pneumothorax. The ViT model was fine-tuned on a dataset of CXRs, which were labelled with advanced preprocessing and augmentation of the data for better generalization. To improve clinical interpretability, we used self-attention maps to develop a more transparent and explainable AI based diagnostic system. In our approach, we showed that our model had superior classification performance with high sensitivity and specificity across both conditions. The self-attention maps provided intrinsic interpretability by highlighting clinically relevant regions in the X-rays, which aligned with expert radiological assessments. The model also had better generalization across datasets, reducing biases typical to CNN based architectures. The results show that ViTs can be a potential new approach for CNNs in the field of medical imaging, especially for the automated interpretation of chest X-rays. Through enhancing the classification accuracy, guaranteeing the domain generalization, and ensuring the clinical interpretability, ViTs can improve the AI-assisted diagnostics in radiology workflows and thus facilitate the fast and accurate decision making in the respiratory disease detection. Future work will include exploring multi-modal fusion approaches and real-world clinical validation to further enhance the effectiveness of transformer-based models in healthcare in practice.