Neuroconnectomics fusion: advancing Alzheimer's disease classification through the integration of GCN-MLP and multifeatured fMRI analysis

Jie Liu; Yibo Huang; Qiuyu Zhang

doi:10.1117/12.3044093

27 August 2024 Neuroconnectomics fusion: advancing Alzheimer's disease classification through the integration of GCN-MLP and multifeatured fMRI analysis

Jie Liu, Yibo Huang, Qiuyu Zhang

Author Affiliations +

Proceedings Volume 13252, Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024); 132520W (2024) https://doi.org/10.1117/12.3044093
Event: 2024 Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024), 2024, Kaifeng, China

Abstract

Deep learning techniques are extensively employed for the classification and diagnosis of Alzheimer's disease (AD). Existing classification methods often use a single feature from functional magnetic resonance imaging (fMRI) and neglect fusion among multiple features. Thus, this paper proposes a framework of GCN combined with MLP model (ie, GCN-MLP) by utilizing fMRI multi-feature analysis, which includes amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and degree centrality (DC), for the classification of AD patients. The KNN algorithm is used to construct the adjacency matrix to capture the connectivity relationship between nodes, thus enabling the GCN to propagate and learn features using this relationship, and output the updated feature matrix for further integration with the MLP model to improve the predictive ability of the model. In addition, an interpretable feature learning method is employed to provide interpretable disease diagnosis information that enhances the interpretability of the model. It is found that the suggested approach achieves a classification accuracy of 95.2% and outperforms existing AD classification methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jie Liu, Yibo Huang, and Qiuyu Zhang "Neuroconnectomics fusion: advancing Alzheimer's disease classification through the integration of GCN-MLP and multifeatured fMRI analysis", Proc. SPIE 13252, Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024), 132520W (27 August 2024); https://doi.org/10.1117/12.3044093

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