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COVID-19 CT Image Segmentation Based on Modified U-Net
- 1 Shandong University of Science and Technology, 579 Qianwangang Road, Huangdao District, Qingdao, Shandong, China
* Author to whom correspondence should be addressed.
Abstract
This paper presents a COVID-19 CT image segmentation method based on a modified U-Net model. In the study and treatment of COVID-19, the segmentation of CT images is crucial for understanding the virus's impact on lung tissues. Traditional image segmentation methods have limitations when dealing with the complex lung lesions caused by COVID-19. Therefore, we optimized the U-Net model's structure and introduced an efficient loss function along with image enhancement techniques to improve segmentation accuracy and computational efficiency. Experiments were conducted on the Medseg and Radiopaedia datasets. The results demonstrate that the modified U-Net model outperforms traditional methods in terms of segmentation accuracy and computational efficiency. Additionally, we discuss the advantages and limitations of the model and propose directions for future research.
Keywords
COVID-19, CT Image Segmentation, U-Net Deep Learning, Medical Image Processing, Loss Function Optimization.
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Cite this article
Zheng,W. (2024). COVID-19 CT Image Segmentation Based on Modified U-Net. Applied and Computational Engineering,104,176-182.
Data availability
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 2nd International Conference on Machine Learning and Automation
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