An efficient lung disease classification from x-ray image using graph convolutional network and transformer

Lung diseases pose a significant global health challenge, requiring timely and accurate diagnosis for effective treatment. This research addresses the limitations of conventional diagnostic methods by proposing a novel approach for lung disease classification from X-ray images, utilizing the combined strengths of Graph Convolutional Network (GCN) and Transformers. Traditional machine learning models often struggle with the complexity and variability of medical imaging data, resulting in limited diagnostic accuracy. Our hybrid model leverages GCN to capture complex spatial relationships within lung tissues, while Transformers manage sequential data and long-range dependencies. This combined approach ensures superior feature extraction and analysis, robust handling of imaging variability, and seamless integration with clinical data. Our system aims to significantly improve diagnostic accuracy by reducing false positives and negatives, providing reliable support for radiologists. The model also incorporates a continuous learning mechanism, allowing it to evolve with new data and advancements in medical imaging technology. By integrating patient-specific factors such as demographics and clinical history, the model enhances the personalization and precision of diagnoses. Extensive validation on the Chest X-ray14 dataset demonstrates our method’s superiority over traditional models, showcasing its potential to transform lung disease diagnosis in clinical settings.