Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review.

Zhao, Zhen; Chuah, Joon Huang; Lai, Khin Wee; Chow, Chee-Onn; Gochoo, Munkhjargal; Dhanalakshmi, Samiappan; Wang, Na; Bao, Wei; Wu, Xiang

Zhao, Zhen; Chuah, Joon Huang; Lai, Khin Wee; Chow, Chee-Onn; Gochoo, Munkhjargal; Dhanalakshmi, Samiappan; Wang, Na; Bao, Wei; Wu, Xiang.

Afiliación

Zhao Z; Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
Chuah JH; Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
Lai KW; Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
Chow CO; Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
Gochoo M; Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Dhanalakshmi S; Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India.
Wang N; School of Automation, Guangdong Polytechnic Normal University, Guangzhou, China.
Bao W; China Electronics Standardization Institute, Beijing, China.
Wu X; School of Medical Information Engineering, Xuzhou Medical University, Xuzhou, China.

Front Comput Neurosci ; 17: 1038636, 2023.

Article en En | MEDLINE | ID: mdl-36814932

Palabras clave

Alzheimer's disease; Magnetic Resonance Imaging; classification; convolutional neural network; deep learning; machine learning; neuroimaging; transformer

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

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