Abstract
INTRODUCTION: Early diagnosis of Alzheimer's Disease (AD) is crucial for improving patient quality of life and treatment outcomes. However, accurately classifying MRI scans of AD remains challenging due to the subtle and spatially complex nature of lesion regions. This study proposes a novel bidirectional spatial attention mechanism to enhance the focus on key pathological features in AD MRI images, aiming to improve classification accuracy and support earlier intervention. METHODS: To enhance model performance, we introduced a customized bidirectional spatial attention module (ATT) integrated into a Swin-Tiny Transformer backbone. Unlike conventional attention methods, the ATT module generates spatial attention maps by adaptively pooling features along both vertical and horizontal orientations, allowing refined adjustment of attention weights across different image regions. Furthermore, to address issues of limited sample size and class imbalance, we employed data augmentation and expansion strategies, enriching the diversity of training data. The model was trained and evaluated on the augmented OASIS1 dataset. RESULTS: The improved Swin-Tiny+ATT model demonstrated significant performance gains across all key metrics on the augmented dataset. Compared to the baseline Swin Transformer, accuracy improved from 84.83% to 87.96%, recall from 89.82% to 91.92%, precision from 85.27% to 91.98%, and the F1 score from 87.26% to 91.89%. These results confirm that the ATT module effectively enhances the model's ability to capture complex spatial features and identify critical lesion regions. DISCUSSION: The proposed Swin-Tiny+ATT model exhibits strong potential for improving MRI-based classification of Alzheimer's Disease. The bidirectional spatial attention mechanism successfully directs the model's focus to relevant anatomical regions, contributing to higher precision and recall. Combined with data augmentation strategies, the approach mitigates class imbalance and enhances generalization. This work provides a promising deep learning framework to support early and accurate diagnosis of AD, with implications for clinical decision-making and personalized treatment planning.