Framework for lung CT image segmentation based on Unet++

Hao, Ziang, Zhang, Jingsi and Li, Lixian 2025. Framework for lung CT image segmentation based on Unet++. Presented at: 5th International Conference on Signal Processing and Machine Learning, Zurich, Switzerland, 20-21 January 2024. Published in: Shiaeles, Stavros ed. Proceedings of the 5th International Conference on Signal Processing and Machine Learning. Applied and Computational Engineering , vol.133 (1) EWA Publishing, pp. 1-7. 10.54254/2755-2721/2025.20592

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Abstract

Recently, the state-of-art models for medical image segmentation is U-Net and their variants. These networks, though succeeding in deriving notable results, ignore the practical problem hanging over the medical segmentation field: overfitting and small dataset. The over- complicated deep neural networks unnecessarily extract meaningless information, and a majority of them are not suitable for lung slice CT image segmentation task. To overcome the two limitations, we proposed a new whole-process network merging advanced UNet++ model. The network comprises three main modules: data augmentation, optimized neural network, parameter fine-tuning. By incorporating diverse methods, the training results demonstrate a significant advantage over similar works, achieving leading accuracy of 98.03% with the lowest overfitting. potential. Our network is remarkable as one of the first to target on lung slice CT images.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Engineering
Publisher:	EWA Publishing
ISBN:	978-1-83558-943-4
Date of First Compliant Deposit:	6 March 2025
Last Modified:	06 Mar 2025 11:23
URI:	https://orca.cardiff.ac.uk/id/eprint/176666

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