A regularized transformer with adaptive token fusion for Alzheimer's disease diagnosis in brain magnetic resonance images

Alzheimer's Disease (AD) poses significant challenges in neuroimaging, where early and accurate diagnosis is essential for timely intervention. Magnetic Resonance Imaging (MRI) serves as a key modality for AD diagnosis, providing detailed anatomical insights without radiation exposure. While traditional Convolutional Neural Networks (CNNs) excel at local feature extraction from MRI data, they fail to capture global contextual information critical for AD detection. To address this limitation, we propose the Lightweight Robust Alzheimer's Disease Vision Transformer (LRAD-ViT), a computationally efficient framework tailored for early AD detection. LRAD-ViT enhances the global learning capabilities of Vision Transformers through a novel adaptive token fusion technique. The proposed method selectively identifies and merges non-essential tokens within brain MRI scans, optimizing computational efficiency without sacrificing diagnostic performance. By dynamically adapting the token structure during model operation, the method prioritizes diagnostically relevant regions in the images. Additionally, randomized learning regularization improves learning dynamics and model robustness. Additionally, randomized learning regularization improves learning dynamics and model robustness. Validation on two brain MRI datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrates that LRAD-ViT achieves a diagnostic accuracy of 93.41 % on AD versus cognitive normal (CN) and 90.95 % on CN versus mild cognitive impairment (MCI), all while reducing computational demands by around 40 %. These metrics substantiate LRAD-ViT's superior diagnostic performance and computational efficiency, making it a promising tool for the early and accurate diagnosis of Alzheimer's Disease, with significant implications for clinical practice.