Unmasking Pneumothorax: Deep Learning Strategies for Precise Classification and Segmentation

Deep learning has become an essential tool in modern healthcare to aid in diagnostic steps, particularly in life-threatening diseases like pneumothorax. Pneumothorax is a condition in which air leaks between the lungs and chest walls (Collapsed lung). This research addresses limitations and offers a thorough analysis of various methods for diagnosing pneumothorax. Using the U-Net architecture with flexible backbones, such as ResNet34, the studies produce notable Dice coefficients, with a maximum efficiency of 0.8574. Strategies such as UNet++ significantly improve segmentation accuracy by combining data from different semantic levels and integrating change maps. The significance of AI pneumothorax identification is highlighted by in-image annotations, yielding a remarkable AUROC value of 0.877. This research presents an end-to-end framework that consists of a two-step procedure that includes segmentation and classification. The CheXNet, a 121-layer DenseNet model is used in the classification module and UNet++ technique is used in the segmentation phase. Chest radiograph images are transformed into detailed outputs by this integrated method, which also highlights the exact regions impacted and indicates the occurrence of pneumothorax. The results provide a sophisticated and precise way for diagnosing pneumothorax, which advances the use of deep learning in healthcare applications.