Table 10 Comparative performance of brain stroke diagnosis models.
From: An effective brain stroke diagnosis strategy based on feature extraction and hybrid classifier
Model | Accuracy (%) | Precision (%) | Recall (%) | F1-score |
|---|---|---|---|---|
Our model (ViT + VGG16 + Ensemble) | 99.6 | 99.0 | 99.0 | 0.9900 ± 0.0000 |
SqueezeNet + MobileNet + CatBoost | 99.1 | 99.0 | 98.8 | 0.9890 |
Pan et al. (2021) | 97.7 | 97.2 | 97.6 | 0.9740 |
Qiu et al. (2020) | 97.9 | 96.5 | 96.8 | 0.9660 |
Yalc¸ın and Vural (2022) | 98.3 | 97.1 | 97.6 | 0.9730 |
Kumaravel et al. (2021) | 98.6 | 97.0 | 97.2 | 0.9710 |
Patel et al. (2023) | 97.7 | 96.5 | 96.7 | 0.9660 |
