Table 11 Comparison with other State-of-the-Art Methodologies.
References | Methodology | Dataset | Classification scope | Accuracy |
|---|---|---|---|---|
Agarwal et al. (2024)20 | Inception V3 | Figshare “Brain Tumor” MRI dataset | Multi-class (glioma / meningioma / pituitary / healthy) | 0.9889 |
Agarwal et al. (2024)20 | Multi-Path CNN | Figshare “Brain Tumor” MRI dataset | Multi-class (glioma / meningioma / pituitary / healthy) | 0.9600 |
Malakouti et al. (2024)36 | LightGBM + Transfer Learning | MRI tumor classification dataset | Binary (tumor vs. non-tumor) | 0.9570 |
Pande et al. (2024)47 | INDEMNIFIER | Diverse MRIs (various sources) | Binary (tumor vs. non-tumor) | 0.9730 |
Yoon et al. (2025)48 | PDCNN | MRI (hybrid ensemble) | Multi-class (tumor-type & healthy) | 0.9485 |
Malebary et al. (2024)49 | Multi-Layer Hybrid U-Net + CNN | MRI segmentation/classification dataset | Multi-class + segmentation | 0.9700 |
Kaur et al. (2025)50 | Transfer-Learning Optimized ResNet152 | MRI brain tumor classification dataset | Binary (tumor vs. non-tumor) | 0.9853 |
Nizamani et al. (2023)51 | FE1-HU-NET | MRI tumor segmentation + classification dataset | Multi-class + segmentation | 0.9870 |
Nizamani et al. (2023)51 | FE2-HU-NET | MRI tumor segmentation + classification dataset | Multi-class + segmentation | 0.9860 |
Nizamani et al. (2023)51 | FE3-HU-NET | MRI tumor segmentation + classification dataset | Multi-class + segmentation | 0.9890 |
Nizamani et al. (2023)51 | FE4-HU-NET | MRI tumor segmentation + classification dataset | Multi-class + segmentation | 0.9760 |
Shoaib et al. (2024)52 | DenseNet201 + PCA + SVM | MRI tumor classification dataset | Binary (tumor vs. non-tumor) | 0.9800 |
Shoaib et al. (2024)52 | DenseNet201 + PCA + MLP | MRI tumor classification dataset | Binary (tumor vs. non-tumor) | 0.9800 |
Tiwari et al. (2024)53 | ANFIS-F-DBN | MRI brain tumor classification dataset | Binary (tumor vs. non-tumor) | 0.9000 |
Khushi et al. (2023)54 | AlexNet architecture with SGD optimiser | BR35H: Brain–Tumor–Detection 2020 (Kaggle) | Binary (tumor vs. non-tumor) | 0.9879 |
XcepFusion (All CNN layers frozen) | CNN-SVM | BR35H: Brain–Tumor–Detection 2020 (Kaggle) | Binary (tumor vs. non-tumor) | 0.9833 |
CNN-DT | BR35H: Brain–Tumor–Detection 2020 | Binary (tumor vs. non-tumor) | 0.9017 | |
CNN-KNN | BR35H: Brain–Tumor–Detection 2020 | Binary (tumor vs. non-tumor) | 0.9850 | |
CNN-RF | BR35H: Brain–Tumor–Detection 2020 | Binary (tumor vs. non-tumor) | 0.9750 | |
CNN-LR | BR35H: Brain–Tumor–Detection 2020 | Binary (tumor vs. non-tumor) | 0.9900 | |
XcepFusion (Pruned CNN layers) | CNN-SVM | BR35H: Brain–Tumor–Detection 2020 | Binary (tumor vs. non-tumor) | 0.9833 |
CNN-DT | BR35H: Brain–Tumor–Detection 2020 | Binary (tumor vs. non-tumor) | 0.9017 | |
CNN-KNN | BR35H: Brain–Tumor–Detection 2020 | Binary (tumor vs. non-tumor) | 0.9883 | |
CNN-RF | BR35H: Brain–Tumor–Detection 2020 | Binary (tumor vs. non-tumor) | 0.9683 | |
CNN-LR | BR35H: Brain–Tumor–Detection 2020 | Binary (tumor vs. non-tumor) | 0.9900 |
