Towards Clinical Diagnoses: Classifying Alzheimer's Disease Using Single fMRI, Small Datasets, and Transfer Learning
This transfer learning model achieved an AD classification accuracy of 77% and outperformed the same model without transfer learning by approximately 30%. Accordingly, our model showed that small AD samples can be accurately classified in a clinically friendly manner.
Authors
- Ahmed A. Moustafa (ORCID: https://orcid.org/0000-0001-5971-273X)
- Samuel L. Warren (ORCID: https://orcid.org/0000-0001-6473-1791)
Institutions
- Bond University (AU)
- University of Johannesburg (ZA)
Publication Details
- Journal
- Brain and Behavior
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1002/brb3.70427
- Citations
- 4
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 5.72
Funders
- Australian Government
- Bond University