UNet segmentation network of COVID-19 CT images with multi-scale attention

Mingju Chen; Sihang Yi; Mei Yang; Zhiwen Yang; Xingyue Zhang; Mingju Chen; Sihang Yi; Mei Yang; Zhiwen Yang; Xingyue Zhang

doi:10.3934/mbe.2023747

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 9: 16762-16785. doi: 10.3934/mbe.2023747

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UNet segmentation network of COVID-19 CT images with multi-scale attention

1.
School of Automation and Information Engineering, Sichuan University of Science & Engineering, Yibin 644002, China
2.
Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science & Engineering, Yibin 644002, China
3.
Zigong Third People's Hospital, Zigong 643000, China

Academic Editor: Yang Kuang

Received: 23 July 2023 Revised: 15 August 2023 Accepted: 16 August 2023 Published: 23 August 2023

In recent years, the global outbreak of COVID-19 has posed an extremely serious life-safety risk to humans, and in order to maximize the diagnostic efficiency of physicians, it is extremely valuable to investigate the methods of lesion segmentation in images of COVID-19. Aiming at the problems of existing deep learning models, such as low segmentation accuracy, poor model generalization performance, large model parameters and difficult deployment, we propose an UNet segmentation network integrating multi-scale attention for COVID-19 CT images. Specifically, the UNet network model is utilized as the base network, and the structure of multi-scale convolutional attention is proposed in the encoder stage to enhance the network's ability to capture multi-scale information. Second, a local channel attention module is proposed to extract spatial information by modeling local relationships to generate channel domain weights, to supplement detailed information about the target region to reduce information redundancy and to enhance important information. Moreover, the network model encoder segment uses the Meta-ACON activation function to avoid the overfitting phenomenon of the model and to improve the model's representational ability. A large number of experimental results on publicly available mixed data sets show that compared with the current mainstream image segmentation algorithms, the pro-posed method can more effectively improve the accuracy and generalization performance of COVID-19 lesions segmentation and provide help for medical diagnosis and analysis.
- COVID-19,
- lesion segmentation,
- multi-scale attention,
- UNet,
- local channel attention,
- Meta-ACON activation
Citation: Mingju Chen, Sihang Yi, Mei Yang, Zhiwen Yang, Xingyue Zhang. UNet segmentation network of COVID-19 CT images with multi-scale attention[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 16762-16785. doi: 10.3934/mbe.2023747

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Abstract

In recent years, the global outbreak of COVID-19 has posed an extremely serious life-safety risk to humans, and in order to maximize the diagnostic efficiency of physicians, it is extremely valuable to investigate the methods of lesion segmentation in images of COVID-19. Aiming at the problems of existing deep learning models, such as low segmentation accuracy, poor model generalization performance, large model parameters and difficult deployment, we propose an UNet segmentation network integrating multi-scale attention for COVID-19 CT images. Specifically, the UNet network model is utilized as the base network, and the structure of multi-scale convolutional attention is proposed in the encoder stage to enhance the network's ability to capture multi-scale information. Second, a local channel attention module is proposed to extract spatial information by modeling local relationships to generate channel domain weights, to supplement detailed information about the target region to reduce information redundancy and to enhance important information. Moreover, the network model encoder segment uses the Meta-ACON activation function to avoid the overfitting phenomenon of the model and to improve the model's representational ability. A large number of experimental results on publicly available mixed data sets show that compared with the current mainstream image segmentation algorithms, the pro-posed method can more effectively improve the accuracy and generalization performance of COVID-19 lesions segmentation and provide help for medical diagnosis and analysis.

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