A Novel Deep Learning Model for Pneumothorax Segmentation in Chest X-Ray Images

Sonam Khattar, Sheenam, Tushar Verma

2025

Abstract

Pneumothorax is a potentially fatal illness that must be identified quickly to prevent serious consequences. Chest X-rays (CXR) are usually used to diagnose it, but manually interpreting the images takes a lot of time and effort. Convolutional Neural Networks (CNNs), a type of deep learning approach, have demonstrated potential in automating this procedure for quicker and more precise diagnoses. Using pre-trained backbone networks-ResNet50, EfficientNet, and XceptionNet-this study creates a segmentation model based on the U-Net architecture. Performance is evaluated using Dice Coefficient, IoU, and pixel-wise accuracy, emphasizing segmentation quality and computational efficiency. Experimental results show that EfficientNet achieves the optimal trade-off between accuracy and resource usage, ResNet50 delivers stable performance with moderate computational needs, and XceptionNet provides superior precision at a higher computational cost. Incorporating Dice Loss and Binary Cross-Entropy enhances stability and robustness against class imbalance. This work demonstrates how backbone selection and augmentation strategies significantly impact medical image segmentation, offering practical insights for developing more accurate and efficient diagnostic models for real-world clinical applications.

Paper Citation

in Harvard Style

Khattar S., Sheenam. and Verma T. (2025). A Novel Deep Learning Model for Pneumothorax Segmentation in Chest X-Ray Images. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 848-853. DOI: 10.5220/0013733900004664

in Bibtex Style

@conference{incoft25,
author={Sonam Khattar and Sheenam and Tushar Verma},
title={A Novel Deep Learning Model for Pneumothorax Segmentation in Chest X-Ray Images},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={848-853},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013733900004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - A Novel Deep Learning Model for Pneumothorax Segmentation in Chest X-Ray Images
SN - 978-989-758-763-4
AU - Khattar S.
AU - Sheenam.
AU - Verma T.
PY - 2025
SP - 848
EP - 853
DO - 10.5220/0013733900004664
PB - SciTePress