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11 September 2024 Neural network integrating graph attention and LSTM based on brain effective connectivity for diagnose of Alzheimer’s disease
Yu Deng, Jiani Li, Yitong Huang, Meiyu Liu
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Proceedings Volume 13270, International Conference on Future of Medicine and Biological Information Engineering (MBIE 2024); 1327019 (2024) https://doi.org/10.1117/12.3039959
Event: 2024 International Conference on Future of Medicine and Biological Information Engineering (MBIE 2024), 2024, Shenyang, China
Abstract
In recent years, progress in deep learning has significantly refined AD/MCI classification, but the relationship between functional connectivity changes and structural connectors remains to be created. To address this issue, this article proposes an inventive diagnostic system that utilizes the brain's effective connectivity network and integrates the Graph Attention Network (GAT) with the Long Short-Term Memory Network (LSTM). with the Long Short-Term Memory Organize (LSTM). By capturing brain interactions and dynamic changes, the framework can progress with demonstrative precision. Utilizing the Alzheimer's Malady Neuroimaging Activity (ADNI) dataset, the framework proved to be excellent at recognizing and predicting Alzheimer's disease, which illustrates the clinical potential it has. This research details the design, implementation and initial validation of the proposed method, emphasizing its effectiveness.
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Yu Deng, Jiani Li, Yitong Huang, and Meiyu Liu "Neural network integrating graph attention and LSTM based on brain effective connectivity for diagnose of Alzheimer’s disease", Proc. SPIE 13270, International Conference on Future of Medicine and Biological Information Engineering (MBIE 2024), 1327019 (11 September 2024); https://doi.org/10.1117/12.3039959
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KEYWORDS
Brain
Data modeling
Alzheimer disease
Neural networks
Diagnostics
Data processing
Mathematical optimization
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