A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network.

Wang, Ruofan; He, Qiguang; Han, Chunxiao; Wang, Haodong; Shi, Lianshuan; Che, Yanqiu

Wang, Ruofan; He, Qiguang; Han, Chunxiao; Wang, Haodong; Shi, Lianshuan; Che, Yanqiu.

Afiliación

Wang R; School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China.
He Q; School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China.
Han C; Tianjin Key Laboratory of Information Sensing and Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China.
Wang H; School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China.
Shi L; School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China.
Che Y; Tianjin Key Laboratory of Information Sensing and Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China.

Front Neurosci ; 17: 1177424, 2023.

Article en En | MEDLINE | ID: mdl-37614342

Palabras clave

2D-CNN; Alzheimer's disease; ROI; fMRI; phase synchronization index

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Front Neurosci Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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