A comparative analysis of deep learning models for lung segmentation on X-ray images

arXiv:2404.06455v1 Announce Type: cross
Abstract: 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 …

abstract analysis arxiv comparative analysis cs.cv cs.lg deep learning diverse eess.iv image images imaging implementation medical medical imaging papers performance ranking ray robust segmentation solutions study type x-ray