Brain Tumor Classification on Figshare (test)
99.46AccuracyOptimized Weighted Voting System
Evaluation results
| Method | Links | |
|---|---|---|
| Optimized Weighted Voting SystemModel Type=Ensemble System, Architecture=KNN, SVM, ResNet50, Xception, CNN-MRI, DenseNet121, ResNet1012026.03 | 99.46 | |
| Vu et al.Model Type=Ensemble System, Architecture=KNN, SVM, CNN-MRI, ResNet502026.03 | 98.36 | |
| Khan et al.Model Type=Single Classifier, Architecture=23-layer CNN2026.03 | 97.8 | |
| Siar et al.Model Type=Ensemble System, Architecture=AlexNet, VGG-16, VGG-19, ResNet502026.03 | 97.55 | |
| Montoya et al.Model Type=Single Classifier, Architecture=ResNet502026.03 | 97.3 | |
| Vu et al.Model Type=Single Classifier, Architecture=ResNet502026.03 | 96.53 | |
| Munira et al.Model Type=Ensemble System, Architecture=23-layer CNN, Random Forest, SVM2026.03 | 96.52 | |
| Momina et al.Model Type=Single Classifier, Architecture=ResNet502026.03 | 95.9 | |
| Shnaka et al.Model Type=Single Classifier, Architecture=R-CNN2026.03 | 94.6 | |
| Dheepak et al.Model Type=Ensemble System, Architecture=SVM with various kernels2026.03 | 93 | |
| Bogacsovics et al.Model Type=Ensemble System, Architecture=AlexNet, MobileNetv2, EfficientNet, ShuffleNetv22026.03 | 92 |