阿尔茨海默症的多模态分类方法

doi:10.3969/j.issn.0255-8297.2023.06.008

Abstract

Abstract: The current grading methods for Alzheimer’s disease（AD）, Early Mild Cognitive Impairment（EMCI）, and Normal Control（NC） suffer from difficulties recognizing EMCI and low multi-classification accuracy. To address these issues, a brain region feature extraction method is proposed, and an AD multi-modal classification model is designed with a fusion of ResNet network. Brain MRI images are spatially registered, segmented by Bayesian and Gaussian mixture models to obtain gray matter, the regions with the greatest difference are selected as the feature image area, and images and biomarkers are processed by the classification model. The proposed method improves performance by at least 5% and achieves an accuracy of 95.5%, 93.5%, and 86.3% for AD＆NC, AD＆EMCI,and AD＆EMCI＆NC classification, respectively, surpassing any single-modal network and verifying the effectiveness of this method.

Key words: magnetic resonance imaging, the greatest difference of brain region, multimodal fusion, clinical markers, ResNet

CLC Number:

TP391.4

LI Weihan, HOU Beiping, HU Feiyang, ZHU Bihong. Multi-modal Diagnosis Method of Alzheimer’s Disease[J]. Journal of Applied Sciences, 2023, 41(6): 1004-1018.

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Multi-modal Diagnosis Method of Alzheimer’s Disease

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