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

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.

💡 Research Summary

The paper introduces PseudoColorViT‑Alz, a novel framework that enhances grayscale brain MRI scans with pseudo‑color transformations and leverages a pretrained Vision Transformer (ViT‑Base) for four‑class Alzheimer’s disease (AD) classification. Recognizing that subtle structural changes in MRI are difficult for conventional convolutional networks to capture, the authors first convert each 224 × 224 grayscale slice into a three‑channel RGB image using the jet colormap. This conversion amplifies intensity variations and texture cues, thereby reducing the domain gap between medical images and the natural‑image data on which ViT was originally trained.

The OASIS‑1 dataset is reorganized into four diagnostic categories—non‑demented, very mild, mild, and moderate dementia—yielding roughly 5 000 samples per class (≈15 500 images total). After resizing, normalizing to