A double ensemble that fuses features from pretrained CNNs and ViTs and ensembles tuned ML classifiers reaches 97.5% to 99.3% accuracy on three public brain MRI datasets, but the gains are not benchmarked against a held-out SOTA baseline.
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Hybrid Ensemble Approaches: Optimal Deep Feature Fusion and Hyperparameter-Tuned Classifier Ensembling for Enhanced Brain Tumor Classification
A double ensemble that fuses features from pretrained CNNs and ViTs and ensembles tuned ML classifiers reaches 97.5% to 99.3% accuracy on three public brain MRI datasets, but the gains are not benchmarked against a held-out SOTA baseline.