现代信息科技2025，Vol.9Issue(16)：76-81,6.DOI:10.19850/j.cnki.2096-4706.2025.16.014
基于rs-fMRI动态特征融合的阿尔茨海默病诊断
Alzheimer's Disease Diagnosis Based on Dynamic Feature Fusion of rs-fMRI
摘要
Abstract
The dynamic Functional Connectivity(dFC)networks based on resting-state functional Magnetic Resonance Imaging(rs-fMRI)provide an important approach for deciphering the pathological mechanisms of Alzheimer's Disease(AD)and Mild Cognitive Impairment(MCI).Aiming at the problems of insufficient modeling of interaction relationships between continuous time-series features and the lack of multi-scale spatio-temporal feature fusion mechanisms in existing Deep Learning methods,a Deep Learning framework based on Dynamic Feature Fusion(DFF)is proposed for the automatic diagnosis of brain diseases using rs-fMRI data.Experimental validation based on rs-fMRI data of 174 subjects from the Alzheimer's Disease Neuroimaging Initiative database shows that the proposed method demonstrates remarkable diagnostic performance in both binary and multi-classification tasks.关键词
动态功能连接/动态特征融合/阿尔茨海默病/静息态功能磁共振成像/脑疾病分类Key words
dynamic Functional Connectivity/dynamic feature fusion/Alzheimer's disease/rs-fMRI/brain disease classification分类
信息技术与安全科学引用本文复制引用
林凯,王睿..基于rs-fMRI动态特征融合的阿尔茨海默病诊断[J].现代信息科技,2025,9(16):76-81,6.基金项目
安徽省职业与成人教育学会2024年教育教学研究规划课题"现代产业学院"背景下人工智能技术应用"现场工程师"人才培养模式研究项目(AZCJ2024129) （AZCJ2024129）
安徽省质量工程技能大师工作室项目(2022jnds024) （2022jnds024）
校级技术技能创新服务平台应用研究项目(2022ZDG01) （2022ZDG01）
