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Framework for Lung CT Image Segmentation Based on UNet++
- 1 Cardiff University
- 2 Northeastern University
- 3 Florida College of Integrative Medicine
* Author to whom correspondence should be addressed.
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.
Keywords
Lung segmentation, medical image segmentation, UNet++, overfitting
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Cite this article
Hao,Z.;Zhang,J.;Li,L. (2025). Framework for Lung CT Image Segmentation Based on UNet++. Applied and Computational Engineering,133,1-7.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning
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