Deep Learning-Based Multilevel Classification of Alzheimer's Disease Using Non-invasive Functional Near-Infrared Spectroscopy.

Ho, Thi Kieu Khanh; Kim, Minhee; Jeon, Younghun; Kim, Byeong C; Kim, Jae Gwan; Lee, Kun Ho; Song, Jong-In; Gwak, Jeonghwan

Ho, Thi Kieu Khanh; Kim, Minhee; Jeon, Younghun; Kim, Byeong C; Kim, Jae Gwan; Lee, Kun Ho; Song, Jong-In; Gwak, Jeonghwan.

Afiliação

Ho TKK; Department of Software, Korea National University of Transportation, Chungju, South Korea.
Kim M; Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, South Korea.
Jeon Y; School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea.
Kim BC; Department of Neurology, Chonnam National University Medical School, Gwangju, South Korea.
Kim JG; Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, South Korea.
Lee KH; Gwangju Alzheimer's Disease and Related Dementias Cohort Research Center, Chosun University, Gwangju, South Korea.
Song JI; Department of Biomedical Science, Chosun University, Gwangju, South Korea.
Gwak J; Korea Brain Research Institute, Daegu, South Korea.

Front Aging Neurosci ; 14: 810125, 2022.

Article em En | MEDLINE | ID: mdl-35557842

Palavras-chave

Alzheimer's disease; CNN-LSTM; deep learning artificial neural network (DL-ANN); fNIRS; multi-class classification

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Front Aging Neurosci Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Coréia do Sul País de publicação: Suíça

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