Abstract
Slice-based models have been widely applied in Alzheimer's disease (AD) identification tasks due to their reduced parameter count and fast inference speed. However, existing slice-based models require additional slice extraction steps and cannot achieve an end-to-end process from MRI to diagnostic results. Moreover, they often rely on Transformer architectures to model inter-slice dependencies, which suffer from quadratic computational complexity. To address these limitations, we propose ViViMZheimer, a slice-based end-to-end model that directly processes 3D MRI data and generates diagnostic predictions. ViViMZheimer integrates a ViViT-inspired spatial encoder with a Mamba-based temporal modeling mechanism, maintaining linear computational complexity while effectively capturing inter-slice dependencies along three spatial orientations. Additionally, a lightweight spatial attention module emphasizes lesion-relevant brain regions, and a gated bottleneck convolution refines key features in later stages of the model. We evaluated ViViMZheimer on the ADNI dataset, where it achieved accuracies of 98.17%, 82.21%, and 83.15% in distinguishing AD vs. cognitively normal (CN), AD vs. mild cognitive impairment (MCI), and CN vs. MCI, respectively. These results demonstrate that ViViMZheimer provides an effective and computationally efficient solution for automated Alzheimer's disease diagnosis from 3D MRI scans.