Dominating Alzheimer's disease diagnosis with deep learning on sMRI and DTI-MD
We propose a simple yet effective multimodal deep convolutional neural network model MADNet that outperforms traditional deep learning methods that use a single-modality dataset for AD diagnosis.
Authors
- 善文 山岸
- Mingkai Zhang (ORCID: https://orcid.org/0009-0001-9846-123X)
- Chao Zhang (ORCID: https://orcid.org/0000-0003-1255-7183)
- Yuxia Li (ORCID: https://orcid.org/0000-0002-2740-8538)
- Guoxin Wang (ORCID: https://orcid.org/0000-0003-2363-8595)
- Guanqun Chen (ORCID: https://orcid.org/0000-0002-8133-834X)
- Feng Yu (ORCID: https://orcid.org/0000-0002-7021-8588)
- Zhiyi Zhou (ORCID: https://orcid.org/0009-0009-6698-080X)
- Yuxin Jin
- Shipeng Dai
Institutions
- Capital Medical University (CN)
- Beijing Chao-Yang Hospital, Capital Medical University (CN)
- Jingdong (China) (CN)
- Zhejiang University (CN)
- Northeastern University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2024-08-15
- DOI
- https://doi.org/10.3389/fneur.2024.1444795
- Citations
- 10
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 3.86
Funders
- National Natural Science Foundation of China
- National Health Commission of the People's Republic of China