Brain Tumor Classification on MRI Brain Tumor Dataset
99.3AccuracyCNN–ELM Hybrid with SHAP Explainability
Evaluation results
| Method | Links | |
|---|---|---|
| CNN–ELM Hybrid with SHAP ExplainabilityEfficiency=High, Author=Kibriya et al. (2024)2025.12 | 99.3 | |
| SE-Lightweight CNNEfficiency=Yes, Author=Ganguly and Ghosh (2025)2025.12 | 99.22 | |
| Ensemble Hybrid CNNEfficiency=Moderate, Author=Talukder et al. (2024)2025.12 | 99.2 | |
| Hybrid CNN + Wavelet FeaturesEfficiency=Moderate, Author=Preetha et al. (2023)2025.12 | 99.16 | |
| DeepTumorNet (GoogLeNet variant)Efficiency=Moderate, Author=Raza et al. (2022)2025.12 | 99.12 | |
| MobileNet (transfer learning)Explainability=None2025.06 | 99 | |
| Quantized Lightweight CNNEfficiency=Yes, Author=Patel et al. (2024)2025.12 | 98.95 | |
| VGG19 + Grad-CAMExplainability=Grad-CAM2025.06 | 98 | |
| ConvAttenMixerExplainability=None2025.06 | 97 | |
| Enhancement + custom CNNExplainability=None2025.06 | 97 | |
| Pretrained InceptionV3Explainability=None2025.06 | 97 | |
| AlexNet + KNNExplainability=None2025.06 | 97 | |
| Revised CNNExplainability=None2025.06 | 96 | |
| CNN features + KNNExplainability=None2025.06 | 95 | |
| Shallow CNN (7 layers)Explainability=None2025.06 | 94 | |
| Deeper CNN (24 layers)Explainability=None2025.06 | 94 | |
| Dual-input CNN (LIME/SHAP)Explainability=LIME, SHAP2025.06 | 85 |