融合注意力机制和轻量级卷积神经网络的胸部CT影像分类方法研究

doi:10.3969/j.issn.0258-8021.2024.04.005

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Abstract CT images of the same disease type can also show differences due to the different severity of the patient′s disease. At present, main clinical diagnosis methods rely on personal ability and past experience of doctors, and the objectivity needs to be enhanced and the efficiency needs to be improved. In view of these problems, we proposed a CT classification network with attention mechanism-parallel lightweight convolutional neural network for CT classification (PC-CTNet). This network mainly consisted of parallel branch channel shuffle (PCS) module and deep-wise efficient shortcut connection (DES) module. PCS module adopted double branches, fused the features under the multi-scale receptive field. DES module used convolution and efficient channel attention to extract effective deep inter-class differentiation information, and alleviated gradient disappearance by shortcut connection. Experiments were conducted on two chest CT datasets, and the results showed that the classification accuracy of the PC-CTNet model reached 98.46% on the collected dataset with 5 988 CT images in different sizes, and 98.75% on the open-source datasets with 194 922 CT images. The performance indicators of PC-CTNet were close to the existing chest CT classification network, and its parameter and computational complexity was about 0.32 M and 75.58 M, respectively, which was 10.17% and 3.21% of the chest CT classification network in the experimental comparison. The proposed network has higher parameter and computational efficiency, can effectively assist doctors in diagnosis and improve diagnostic efficiency and objectivity.

Key words： attention mechanism chest CT image convolutional neural network PC-CTNet

Received: 14 November 2022

PACS:

R318

Corresponding Authors: ^*E-mail: wangxin@csust.edu.cn

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	Wang Wei
	Xu Yuyan
	Wang Xin
	Huang Wendi
	Yuan Ping

Cite this article:

Wang Wei,Xu Yuyan,Wang Xin, et al. Research on Chest CT Image Classification Method Combining Attention Mechanism and Lightweight Convolutional Neural Network[J]. Chinese Journal of Biomedical Engineering, 2024, 43(4): 429-437.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2024.04.005 OR http://cjbme.csbme.org/EN/Y2024/V43/I4/429

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