alzheimer's disease recognition classification study using mri images based on deep learning and dual multilayer attention mechanisms

Introduction: current deep learning-based computer-aided diagnosis (cad) techniques face challenges in hierarchical feature extraction and computational efficiency. traditional convolutional neural networks (cnn) often focus on local or single-scale information, neglecting global correlations of brain atrophy and multiscale pathological features. additionally, the parameter explosion problem in deep networks limits model's generalization ability on small and medium-sized datasets. while the introduction of attention mechanisms has significantly improved feature extraction and enhanced cnn recognition capabilities, existing attention mechanisms are mostly single-scale, focusing on feature maps at specific hierarchical levels and ignoring the correlations between features of different layers.material and methods: to address these issues, this study proposes a lightweight model combining a shallow feature pyramid cnn with a dual multi-level attention (dma) mechanism. experiments using the public oasis-1 dataset, which contains 86,437 mri images across 4 categories, employ a focal loss function to handle class imbalance.results: the results show that the model including dma outperforms both the baseline cnn and the single-scale attention mechanism in terms of accuracy (acc), sensitivity (sen), and specificity (spe). specifically, compared to cnn and cnn+cbam: acc improved by 3.33% and 1.26%, sen improved by 13.2% and 0.9%, and spe improved by 1%.conclusion: the model demonstrates significant advantages in distinguishing small-sample classes and differentiating between very mild dementia and normal controls, highlighting its superiority in fine-grained pathological discrimination.