| Large-scale MRI dataset | BFS+DFE+DR (Proposed EDSH framework) | Accuracy0.985 | | 25 | 3mo ago |
| MRI Brain Tumor Dataset | | Accuracy99.3 | | 17 | 2mo ago |
| 7K-DS | DB-FGA-Net | Accuracy99.85 | | 11 | 1mo ago |
| Figshare (test) | Optimized Weighted Voting System | Accuracy99.46 | | 11 | 2mo ago |
| Target dataset | Orientation-Aware UDA Framework | Macro F1 Score72.95 | | 10 | 28d ago |
| Source dataset | Orientation-Aware UDA Framework | Macro F1 Score98.43 | | 10 | 28d ago |
| Kaggle Brain Tumor | KNN, SVM, ResNet50, Xception, CNN-MRI, DenseNet121, ResNet101 (Proposed Method) | Accuracy99.85 | | 10 | 2mo ago |
| BraTS 50% labels (val) | | Sensitivity84.8 | | 9 | 3mo ago |
| BraTS full labels (cross-validation) | Trad.+3DSiam+RE | Sensitivity92 | | 9 | 3mo ago |
| TCGA | PM-EF_WG | Oligo Precision77.9 | | 8 | 3mo ago |
| Dataset 1 | Proposed CNN | Accuracy99.03 | | 7 | 20d ago |
| Dataset 2 | Proposed CNN | Accuracy99.28 | | 6 | 20d ago |
| Figshare | EfficientNetV2–MLP Mixer Attention | Precision99.47 | | 5 | 1mo ago |
| Glioma dataset primary vs. secondary brain tumor (test) | 3D Vanilla-ViT | AUC95.7 | | 5 | 3mo ago |
| Figshare 3 class | | Accuracy99.06 | | 4 | 1mo ago |
| Tumor dataset | FHE-DiCSNN | Run time (s)0.34 | | 4 | 2mo ago |
| Combined 4 datasets | | Accuracy99.5 | | 3 | 1mo ago |
| Brain Tumor Classification (MRI) Four Classes | | Accuracy93.44 | | 3 | 2mo ago |
| MRI Brain Tumor Dataset 3000 samples | | Accuracy98 | | 2 | 2mo ago |
| MRI Brain Tumor Dataset 3064 samples | | Accuracy98 | | 2 | 2mo ago |
| BraTS 2019 | | Accuracy99 | | 2 | 2mo ago |
| 13K-DS | | Accuracy98.18 | | 1 | 1mo ago |
| 7K-DS 2-Class | DB-FGA-Net | Accuracy99.85 | | 1 | 1mo ago |
| 7K-DS 3-Class | DB-FGA-Net | Accuracy98.68 | | 1 | 1mo ago |
| MRI Brain Tumor Dataset 150 samples | | Accuracy93 | | 1 | 2mo ago |
