LRSAR-Net Semantic Segmentation Model for Computer Aided Diagnosis for Covid-19 CT Image

ZHANG Taohong; GUO Xuxu; ZHANG Ying

doi:10.11999/JEIT210917

Volume 44 Issue 1

Jan. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(1): 48-58

ZHANG Taohong, GUO Xuxu, ZHANG Ying. LRSAR-Net Semantic Segmentation Model for Computer Aided Diagnosis for Covid-19 CT Image[J]. Journal of Electronics & Information Technology, 2022, 44(1): 48-58. doi: 10.11999/JEIT210917

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ZHANG Taohong, GUO Xuxu, ZHANG Ying. LRSAR-Net Semantic Segmentation Model for Computer Aided Diagnosis for Covid-19 CT Image[J]. Journal of Electronics & Information Technology, 2022, 44(1): 48-58. doi: 10.11999/JEIT210917

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LRSAR-Net Semantic Segmentation Model for Computer Aided Diagnosis for Covid-19 CT Image

doi: 10.11999/JEIT210917

ZHANG Taohong^{1, 2
,
,},
GUO Xuxu^{1, 2},
ZHANG Ying³

1.
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Beijing Key Laboratory of Knowledge Engineering for Materials Science., Beijing 100083, China
3.
QingGong College, North China University of Science and Technology, Tangshan 064000, China

Funds: The National Key Research and Development Program of China (2018YFC1707410)

Received Date: 2021-09-01
Accepted Date: 2021-12-24
Rev Recd Date: 2022-12-01

Available Online: 2021-12-30

Publish Date: 2022-01-10

Abstract

Abstract

Since the outbreak of the Covid-19 epidemic in the world in late 2019, all countries in the world are under the threat of epidemic. Covid-19 invades the body's respiratory system, causing lung infection or even death. Computed Tomography (CT) is a routine method for doctors to diagnose patients with pneumonia. In order to improve the efficiency of doctors in diagnosing patients with new crown infection, this paper proposes a semantic segmentation network LRSAR-Net based on low rank tensor self-attention reconstruction, in which the low rank tensor self-attention reconstruction module is used to obtain long-range information. The low rank tensor self-attention reconstruction module mainly includes three parts: low rank tensor generation sub module, low rank self-attention sub module and high rank tensor reconstruction module. The low rank tensor self-attention module is divided into multiple low rank tensors, the low rank self-attention feature map is constructed, and then the multiple low rank tensor attention feature maps are reconstructed into a high rank attention feature map. The self-attention module obtains long-range semantic information by calculating the similarity matrix. Compared with the traditional self-attention module Non Local, the low rank tensor self-attention reconstruction module has lower computational complexity and faster computing speed. Finally, this paper compares with other excellent semantic segmentation models to reflect the effectiveness of the model.
- Semantic segmentation,
- Medical diagnosis,
- Convolutional Neural Network (CNN),
- Tensor reconstruction,
- Self-attention mechanism

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References(34)

References

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