LEFF-ViT: A locally enhanced vision transformer framework for accurate Alzheimer's Disease classification from brain MRI.

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Abstract

Early and accurate diagnosis of Alzheimer's Disease (AD) is critical for effective disease management and progression delay. Researches have been done in past towards better study of Alzheimer's, but advancements in feature engineering-cum-learning methodologies have still created scope to overcome the limits of previous methods and achieve more accurate modelling and classification. Here, we propose a novel model, LEFF-ViT (Locally Enhanced Feedforward Vision Transformer), for AD classification along with a framework culminating an idea of using separate segmented brain subregions as a marked feature engineering element. For this Segmentation of MRI images are done to extract White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF) regions using a Deep Residual Squeeze-Inception U-Net (De-RIS U-Net). Subsequently, a novel DWFE-Net is employed to extract discriminative spatial features. Finally, LEFF-ViT integrates a Vision Transformer with Multi-Head Self-Attention and a Locally Enhanced Feedforward Network (LFFN) to effectively capture both local and global contextual information for accurate classification. The experimental results demonstrate that the proposed model achieves an accuracy of 98.68 %, a sensitivity of 96.45 %, a specificity of 98.17 %, a Dice score of 96.36 %, and a Jaccard index of 92.31 %, which nearly outperforms the existing state-of-the-art methods across multiple evaluation metrics.

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