Lung Segmentation

Lung segmentation, the automated identification and delineation of lung regions in medical images (primarily X-rays and CT scans), aims to improve the accuracy and efficiency of diagnosing and treating lung diseases. Current research heavily utilizes deep learning, employing architectures like U-Net, its variants (e.g., TransUNet, Res-U-Net), and transformers, often incorporating attention mechanisms to enhance feature extraction and segmentation precision. This work is crucial for improving diagnostic accuracy, enabling faster screening, and facilitating the development of computer-aided diagnosis systems, particularly beneficial in resource-constrained settings.