AlzFormer: Video-based space-time attention model for early diagnosis of Alzheimer's disease.
Authors
Affiliations (5)
Affiliations (5)
- Department of Medicine, LSU Health Shreveport, Shreveport, LA, USA; Department of Software Engineering, Faculty of Engineering, Istanbul Topkapı University, Istanbul, Turkey.
- Department of Computer Engineering, Faculty of Engineering, Istanbul Galata University, Istanbul, Turkey.
- Department of Artificial Intelligence Engineering, Trabzon 61335, Turkey.
- Department of Neurosurgery, LSU Health Shreveport, Shreveport, LA, USA.
- Department of Medicine, LSU Health Shreveport, Shreveport, LA, USA. Electronic address: [email protected].
Abstract
Early and accurate Alzheimer's disease (AD) diagnosis is critical for effective intervention, but it is still challenging due to neurodegeneration's slow and complex progression. Recent studies in brain imaging analysis have highlighted the crucial roles of deep learning techniques in computer-assisted interventions for diagnosing brain diseases. In this study, we propose AlzFormer, a novel deep learning framework based on a space-time attention mechanism, for multiclass classification of AD, MCI, and CN individuals using structural MRI scans. Unlike conventional deep learning models, we used spatiotemporal self-attention to model inter-slice continuity by treating T1-weighted MRI volumes as sequential inputs, where slices correspond to video frames. Our model was fine-tuned and evaluated using 1.5 T MRI scans from the ADNI dataset. To ensure the anatomical consistency of all the MRI data, All MRI volumes were pre-processed with skull stripping and spatial normalization to MNI space. AlzFormer achieved an overall accuracy of 94 % on the test set, with balanced class-wise F1-scores (AD: 0.94, MCI: 0.99, CN: 0.98) and a macro-average AUC of 0.98. We also utilized attention map analysis to identify clinically significant patterns, particularly emphasizing subcortical structures and medial temporal regions implicated in AD. These findings demonstrate the potential of transformer-based architectures for robust and interpretable classification of brain disorders using structural MRI.