Effective deep convolutional neural network with attention mechanism for Alzheimer disease classification
The visualizations of attention mechanism are used to increase the interpretability and demonstrate the attention maps which are align with known AD biomarkers. These results indicates that the attention-guided deep models can both improve multi-class MRI classification accuracy and provide clinically useful regional explanations.
Authors
- Aanjankumar Sureshkumar
- Md Shohel Sayeed (ORCID: https://orcid.org/0000-0002-0052-4870)
- Sridhar Lakshmanan (ORCID: https://orcid.org/0000-0001-7387-3943)
- Mohamed Yasin Noor Mohamed (ORCID: https://orcid.org/0000-0002-3133-8720)
- Maragatharajan Muthusamy
- Rajesh Kumar Dhanaraj (ORCID: https://orcid.org/0000-0002-2038-7359)
- Gopal Rathinam (ORCID: https://orcid.org/0000-0002-6935-9367)
Institutions
- SRM Institute of Science and Technology (IN)
- Multimedia University (MY)
- Symbiosis International University (IN)
- Al-Buraimi University College (OM)
- University of Buraimi (OM)
- Cloud Computing Center (CN)
- Barkatullah University (IN)
- Sultan Qaboos University (OM)
Publication Details
- Journal
- Frontiers in Radiology
- Published
- 2026-01-14
- DOI
- https://doi.org/10.3389/fradi.2025.1698760
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00