A lightweight vision transformer with context-aware convolution and uniformity normalization for Alzheimer’s Disease diagnosis

Background Early and accurate diagnosis of Alzheimer’s Disease (AD) is crucial for effective clinical intervention. Method In this study, we propose a lightweight vision transformer architecture specifically designed for AD classification using 2D brain MRI slices. LICAUN-ViT incorporates three key innovations: Mono-Head Self-Attention (MOHSA) to reduce computational overhead, Uniformity Normalization (Uni-Norm) to mitigate oversmoothing and enhance feature diversity, and Context-Aware Convolution (CAC) to integrate long-range dependencies with local structural features. Results Evaluated on two benchmark datasets derived from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), our model achieves state-of-the-art performance with an accuracy of 93.03 % on axial slices and 94.15 % on sagittal slices, while maintaining relatively low floating-point operations (FLOPs) for efficient deployment. Extensive ablation studies and singular value analyses confirm the effectiveness and robustness of the proposed components. Conclusion These results demonstrate that the proposed model offers a computationally efficient and promising solution for automated AD diagnosis, with strong potential for clinical integration.