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Vision Transformers show robust Alzheimer's MRI classification across datasets

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv evaluates the robustness of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for classifying Alzheimer's disease from MRI scans. Researchers compared five architectures, including ResNet-50, ReXNet-150, Swin Transformer, ViT-Base, and ViT-Large, across three diverse MRI datasets. The findings indicate that ViT-Large consistently achieved the highest performance, reaching up to 99.84% accuracy, and that performance differences between models were most apparent when dealing with distribution mismatches between training and testing data. AI

IMPACT ViT-Large demonstrates superior and consistent performance in Alzheimer's MRI classification, highlighting the importance of cross-dataset evaluation for deep learning models.

RANK_REASON Research paper published on arXiv evaluating model performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Vision Transformers show robust Alzheimer's MRI classification across datasets

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Research paper published on arXiv evaluating model performance. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Hafija Aktar, Molly Croes, Frank S. Menniti, Abdeltawab Hendawi ·

    How Robust Are CNNs and Vision Transformers for Alzheimer's MRI Classification Across Datasets?

    arXiv:2610.33416v1 Announce Type: new Abstract: Accurate assessment of Alzheimer's disease stages from MRI is important for understanding disease progression and supporting the development of computer-aided analysis tools. Deep learning models have shown promising results for Alz…

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