Multimodal 3D Deep Learning for Early Diagnosis of Alzheimer’s Disease-Reference-Cited by-同舟云学术

Multimodal 3D Deep Learning for Early Diagnosis of Alzheimer’s Disease

Published:2024 Issue: Volume:12 Page:46278-46289
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Kim Seung Kyu¹^ORCID,Duong Quan Anh¹^ORCID,Gahm Jin Kyu²^ORCID

Affiliation:

1. Department of Information Convergence Engineering, Pusan National University, Busan, South Korea

2. School of Computer Science and Engineering, Pusan National University, Busan, South Korea

Funder

Institute of Information & Communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development grant

Korean Government [Ministry of Science and ICT (MSIT)]

National Research Foundation of Korea (NRF) grant funded by Korean Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10478896.pdf?arnumber=10478896

Reference50 articles.

1. Alzheimer’s disease prevention: from risk factors to early intervention

2. Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease

3. Forecasting the global burden of Alzheimer's disease

4. Clinical trials of new drugs for Alzheimer disease

5. Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review

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