  Robust and highly accurate lung segmentation in X-rays is crucial in medical
imaging. This study evaluates deep learning solutions for this task, ranking
existing methods and analyzing their performance under diverse image
modifications. Out of 61 analyzed papers, only nine offered implementation or
pre-trained models, enabling assessment of three prominent methods: Lung VAE,
TransResUNet, and CE-Net. The analysis revealed that CE-Net performs best,
demonstrating the highest values in dice similarity coefficient and
intersection over union metric.
