A Graph Convolutional Network Based on Univariate Neurodegeneration Biomarker for Alzheimer's Disease Diagnosis

Objective: Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease that is not easily detectable in the early stage. This study proposed an efficient method of applying a graph convolutional network (GCN) on the early prediction of AD. Methods: We proposed a univari...

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Veröffentlicht in:	IEEE journal of translational engineering in health and medicine Jg. 11; S. 405 - 416
Hauptverfasser:	Qu, Zongshuai, Yao, Tao, Liu, Xinghui, Wang, Gang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Abnormalities Accuracy Alzheimer Disease - diagnosis Alzheimer's disease Artificial neural networks Atrophy attention module Biomarkers Cerebral cortex Classification Convolutional neural networks graph convolutional network Humans Magnetic resonance imaging Magnetic Resonance Imaging - methods Medical imaging Modules Morphology Neurodegeneration Neurodegenerative Diseases Neuroimaging Neuroimaging - methods Signs and symptoms Similarity Surface morphology univariate neurodegeneration biomarker graph convolutional network univariate neurodegeneration biomarker magnetic resonance imaging Alzheimer’s disease attention module
ISSN:	2168-2372, 2168-2372
Online-Zugang:	Volltext
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