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

Weronika Hryniewska-Guzik, Jakub Bilski, Bartosz Chrostowski, Jakub Drak Sbahi, Przemysław Biecek
4/9/2024
eess.IVcs.CVcs.LG

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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 metric.

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Cite this paper

@article{hryniewskaguzik2024comparative,
  title  = {A comparative analysis of deep learning models for lung segmentation on X-ray images},
  author = {Weronika Hryniewska-Guzik and Jakub Bilski and Bartosz Chrostowski and Jakub Drak Sbahi and Przemysław Biecek},
  year   = {2024},
  eprint = {2404.06455},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.06455v1}
}

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