A Dual-path Network Chest Film Disease Classification Method Combined with a Triple Attention Mechanism

LI Qiang; WANG Xu; GUAN Xin

doi:10.11999/JEIT220172

Volume 45 Issue 4

Apr. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(4): 1412-1425

LI Qiang, WANG Xu, GUAN Xin. A Dual-path Network Chest Film Disease Classification Method Combined with a Triple Attention Mechanism[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1412-1425. doi: 10.11999/JEIT220172

Citation:

LI Qiang, WANG Xu, GUAN Xin. A Dual-path Network Chest Film Disease Classification Method Combined with a Triple Attention Mechanism[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1412-1425. doi: 10.11999/JEIT220172

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A Dual-path Network Chest Film Disease Classification Method Combined with a Triple Attention Mechanism

doi: 10.11999/JEIT220172

School of Microelectronics, Tianjin University, Tianjin 300072, China

Funds: The National Natural Science Foundation of China (61471263, 61872267, 62071323), The Natural Science Foundation of Tianjin(16JCZDJC31100), The Scientific and Technological Project of Tianjin (20YDTPJC01110), The Seed Foundation of Tianjin University (2021XZC-0024)

Received Date: 2022-02-22
Accepted Date: 2022-08-02
Rev Recd Date: 2022-07-27

Available Online: 2022-08-05

Publish Date: 2023-04-10

Abstract

Abstract

In recent years, medical image processing with CNN has made remarkable research progress in the task of chest film disease classification. However, compared with single structure CNN, dual-path network can combine the characteristics of different CNN to improve the ability of disease classification. Secondly, for different diseases, their location, size, shape, and texture are different, the attention mechanism helps the model to extract different pathological features and improve the classification accuracy. Therefore, focusing on the chest film disease classification problem, a dual path convolution neural network TADPN(Triple Attention Dual Path Network) combined with a triple attention mechanism is proposed. TADPN takes the dual-path network combined with ResNet and DenseNet as the backbone network and uses three different forms of attention mechanisms to improve the backbone network. The network complexity and classification accuracy are improved while maintaining the stability of the parameters. In this paper, the validity of TADPN is compared with the six advanced algorithms on the ChestXray14 dataset. The experiments show the progressiveness of the dual-path CNN and the triple attention mechanism, as well as the effectiveness of TADPN. The average AUC value of 14 diseases reaches 0.8185, which is 1.1% higher than that of previous generations.
- Medical image processing,
- Chest film classification,
- CNN,
- Attention mechanism

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References(40)

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