Objective:

To propose a lightweight hybrid architecture for classifying Alzheimer's disease using MRI imaging.

Approach:

Key Findings:

• HDFT-MViT achieved classification accuracies of 98.85 ±0.27% on the ADNI-1 dataset and 98.07 ± 0.54% on the ADNI-2 dataset.
• The model maintains a lightweight profile with only 3.46 M parameters.

Interpretation:

HDFT-MViT effectively balances local detail perception and global semantic understanding within a computationally efficient framework.

Conclusion:

HDFT-MViT presents a promising tool for clinical Alzheimer's disease diagnosis, with code to be released upon acceptance.

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