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CT Image Segmentation Network of COVID-19 Based on Multi-scale Attention and Probability Preserving Pooling

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DOI: 10.23977/jipta.2023.060105 | Downloads: 10 | Views: 398

Author(s)

Zhiwen Yang 1,2, Mingju Chen 1,2

Affiliation(s)

1 School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong, 643000, China
2 Artificial Intelligence Key Laboratory of Sichuan Province, Yibin, 644000, China

Corresponding Author

Mingju Chen

ABSTRACT

In order to effectively improve the accuracy of U-Net segmentation of COVID-19 CT images, a novel COVID-19 CT image segmentation model based on multi-scale attention and class probability Preserving pooling auxiliary classifier was proposed. In view of the shortcomings of U-Net in using single-size standard two-dimensional convolution to extract image feature information, a multi-scale attention module is adopted to enhance the performance of U-Net in extracting multi-scale information by using three groups of attention modules mixed with different sizes of deep strip convolution and spatial domain channel domain. Aiming at the disadvantage of information loss when using maximum pooling in the U-Net lower sampling layer, two-dimensional convolution is used to reduce the size of the feature map, and an auxiliary classifier is used to calculate the coarse segmentation semantic probability map of each layer of the encoder and the loss is calculated by using the GT tag map of the class probability Preserving pooling to optimize the classification performance of the network. The network model is tested for image segmentation in the standard COVID-19 CT image dataset, and the segmentation accuracy for the infected area is 87.22%, the recall rate is 84.55%, and the intersection/merger ratio is 75.98%.

KEYWORDS

COVID-19, deep learning, image segmentation, multiscale attention, class probability preserving pooling

CITE THIS PAPER

Zhiwen Yang, Mingju Chen, CT Image Segmentation Network of COVID-19 Based on Multi-scale Attention and Probability Preserving Pooling. Journal of Image Processing Theory and Applications (2023) Vol. 6: 54-61. DOI: http://dx.doi.org/10.23977/jipta.2023.060105.

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