CycleGAN-Based Approach for Enhanced Segmentation of Lung Disease Areas in Chest X-Ray Images

In this study, we develop a new segmentation approach based on CycleGAN model to generate healthy lung images from pathological chest X-ray images, followed by image subtraction and binarization to produce a mask that includes pathology-affected areas. This approach enables the extraction of radiomic features from regions containing pathologies, enhancing disease classification. Our segmentation approach demonstrated effectiveness, improving AUC by 10.92% over conventional segmentation method for classifying effusion and infiltration using the XGBoost model, and outperforming previous studies. This study underscores the importance of precise pathological mask generation for accurate lung disease classification.