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- Title
Fully Automated Construction of a Deep U-Net Network Model for Medical Image Segmentation.
- Authors
Daoqing Gong; Jiayan Yang; Xinyan Gan; Xiang Gao; Yuanxia Zhang
- Abstract
In recent years, the use of deep learning technology for image processing has become mainstream, and the U-Net network has received widespread attention owing to its unique U-shaped structure, which has achieved excellent results in the field of image segmentation, especially in medical image segmentation. To enhance the performance of the U-Net network model and establish better U-Net design variables, in this paper, we propose a fuzzy-controlled multicellular gene expression programming algorithm to automatically build and optimize the U-Net. The algorithm creates an efficient variable-length gene code, generates chromosomes for the optimization of U-Net design variables, decodes the chromosomes to construct the U-Net model, dynamically calculates population fitness and fuzzy affiliation values, and achieves the optimal U-Net network through continuous evolution. The experimental results indicate that the proposed algorithm outperforms U-Net, Fully Convolutional Networks32, and VanillaUnet in image recognition segmentation, especially in the segmentation of COVID-19 CT medical images.
- Subjects
COMPUTER-assisted image analysis (Medicine); IMAGE segmentation; DEEP learning; DIAGNOSTIC imaging; IMAGE recognition (Computer vision); COMPUTED tomography; PATTERN matching; IMAGE processing
- Publication
Sensors & Materials, 2023, Vol 35, Issue 10, Part 2, p4633
- ISSN
0914-4935
- Publication type
Article
- DOI
10.18494/SAM4587