Table 1 Accuracy and recall rates of CVTC across different datasets
From: A lightweight CVTC model for accurate Alzheimer’s MRI analysis and lesion annotation
Datasets | Accuracy | Recall | Precision | F1-score |
|---|---|---|---|---|
Kaggle | 99.61% | 1 | 99.75% | 99.77% |
OASIS-1 | 98.16% | 98.54% | 99.16% | 98.85% |
Pseud-RGB dataset | 92.96% | 99.35% | 93.02% | 92.67% |
ADNI-general | 98.80% | 99.82% | 98.76% | 99.88% |
ADNI subtype | 98.51% | 99.83% | 98.89% | 99.70% |
NACC | 96.30% | 90.04% | 95.13% | 91.41% |
MS | 99.65% | 1 | 99.68% | 99.86% |
TS | 99.67% | 99.92% | 99.79% | 99.76% |
