Abstract
Early diagnosis of Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI), is critical for effective prevention and treatment. Computer-aided diagnosis using magnetic resonance imaging (MRI) provides a cost-effective and objective approach. However, existing methods often segment 3D MRI images into 2D slices, leading to spatial information loss and reduced diagnostic accuracy. To overcome this limitation, we propose TA-SSM Net, a deep learning model that leverages tri-directional attention and structured state-space model (SSM) for improved MRI-based diagnosis of AD and MCI. The tri-directional attention mechanism captures spatial and contextual information from forward, backward, and vertical directions in 3D MRI images, enabling effective feature fusion. Additionally, gradient checkpointing is applied within the SSM to enhance processing efficiency, allowing the model to handle whole-brain scans while preserving spatial correlations. To evaluate our method, we construct a dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), consisting of 300 AD patients, 400 MCI patients, and 400 normal controls. TA-SSM Net achieved an accuracy of 90.24% for MCI detection and 95.83% for AD detection. The results demonstrate that our approach not only improves classification accuracy but also enhances processing efficiency and maintains spatial correlations, offering a promising solution for the diagnosis of Alzheimer's disease.