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| Course Classification of Cognitive Disorders Based on Improved Attention Mechanism |
| LI Mei-mei1,2,HU Chun-hai1,ZHOU Ying2,SONG Xin2 |
1. Institute of Electrical Engineering,Yanshan University, Qinhuangdao, Hebei 066004, China
2. Northeastern University at Qinhuangdao, Qinhuangdao, Hebei 066004, China |
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Abstract Alzheimer's disease (AD) is a neurodegenerative disease with a slow onset process and continuous deterioration over time. With the trend of aging, the number of AD patients is increasing. Therefore, how to make an early accurate diagnosis and positive intervention is an urgent problem to be solved. In order to improve the efficiency of computer-aided diagnosis and promote the study of pathophysiological mechanism of diseases, an improved two-dimension dual-path fusion network based on the SE module is proposed. A reduction coefficient module is added to the network to increase the proportion of useful information in images. The weight function of the channel attention module is redesigned to increase the difference between feature maps, and the network is combined with the two-dimensional dual-path network to increase the network emphasis, so as to achieve better classification performance and prevent model overfitting. The ADNI dataset is used to classify AD, EMCI and NC. The experimental results show that the accuracy of the proposed model is improved by 5.59% and 8.11% compared with the VGG and two-dimensional dual-path fusion model, respectively, which verifies the feasibility of the proposed method.
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Received: 26 September 2022
Published: 21 February 2023
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2023, Vol. 44 
