Chest X-ray Pneumothorax Segmentation Using EfficientNet-B4 Transfer Learning in a U-Net Architecture

Alvaro Aranibar Roque, Helga Sebastian
9/4/2025
cs.CVcs.AI

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Abstract

Pneumothorax, the abnormal accumulation of air in the pleural space, can be life-threatening if undetected. Chest X-rays are the first-line diagnostic tool, but small cases may be subtle. We propose an automated deep-learning pipeline using a U-Net with an EfficientNet-B4 encoder to segment pneumothorax regions. Trained on the SIIM-ACR dataset with data augmentation and a combined binary cross-entropy plus Dice loss, the model achieved an IoU of 0.7008 and Dice score of 0.8241 on the independent PTX-498 dataset. These results demonstrate that the model can accurately localize pneumothoraces and support radiologists.

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Cite this paper

@article{roque2025chest,
  title  = {Chest X-ray Pneumothorax Segmentation Using EfficientNet-B4 Transfer Learning in a U-Net Architecture},
  author = {Alvaro Aranibar Roque and Helga Sebastian},
  year   = {2025},
  eprint = {2509.03950},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.03950v1}
}

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