Colormap-Enhanced Vision Transformers for MRI-Based Multiclass (4-Class) Alzheimer's Disease Classification

TL;DR

This paper introduces PseudoColorViT-Alz, a colormap-enhanced Vision Transformer framework that significantly improves MRI-based Alzheimer's disease classification accuracy by leveraging pseudo-color representations to amplify discriminative features.

Contribution

The work presents a novel colormap-enhanced Vision Transformer approach for MRI classification, achieving state-of-the-art accuracy and demonstrating the effectiveness of pseudo-color augmentation.

Findings

Achieved 99.79% accuracy on OASIS-1 dataset

Surpassed recent CNN and Siamese network methods

Enhanced feature extraction with pseudo-color transformation

Abstract

Magnetic Resonance Imaging (MRI) plays a pivotal role in the early diagnosis and monitoring of Alzheimer's disease (AD). However, the subtle structural variations in brain MRI scans often pose challenges for conventional deep learning models to extract discriminative features effectively. In this work, we propose PseudoColorViT-Alz, a colormap-enhanced Vision Transformer framework designed to leverage pseudo-color representations of MRI images for improved Alzheimer's disease classification. By combining colormap transformations with the global feature learning capabilities of Vision Transformers, our method amplifies anatomical texture and contrast cues that are otherwise subdued in standard grayscale MRI scans. We evaluate PseudoColorViT-Alz on the OASIS-1 dataset using a four-class classification setup (non-demented, moderate dementia, mild dementia, and very mild dementia). Our model achieves a state-of-the-art accuracy of 99.79% with an AUC of 100%, surpassing the performance of recent 2024--2025 methods, including CNN-based and Siamese-network approaches, which reported accuracies ranging from 96.1% to 99.68%. These results demonstrate that pseudo-color augmentation combined with Vision Transformers can significantly enhance MRI-based Alzheimer's disease classification. PseudoColorViT-Alz offers a robust and interpretable framework that outperforms current methods, providing a promising tool to support clinical decision-making and early detection of Alzheimer's disease.