Table 6 Performance evaluation of the proposed FDEIoL using various DL models on MRI images dataset.
Classifiers | Evaluation Parameters | Classes | Overall Accuracy | Error Rate | Cohen’s Kappa | Average F1Score | |||
Glioma | Meningioma | Pituitary | No Tumor | ||||||
HIncV3XGBoost | Precision | 0.99 | 0.93 | 0.99 | 0.99 | 0.975 | 0.025 | 0.9667 | 0.975 |
Recall | 0.98 | 0.99 | 0.95 | 0.98 | |||||
F1-Score | 0.98 | 0.96 | 0.97 | 0.99 | |||||
Specificity | 0.99 | 0.97 | 1.00 | 0.99 | |||||
LT-ViT | Precision | 0.97 | 0.93 | 0.94 | 0.99 | 0.955 | 0.045 | 0.9879 | 0.9575 |
Recall | 0.96 | 0.92 | 0.97 | 0.98 | |||||
F1-Score | 0.96 | 0.92 | 0.96 | 0.99 | |||||
Specificity | 0.99 | 0.97 | 0.97 | 0.99 | |||||
BM-Net | Precision | 1.00 | 0.98 | 0.97 | 0.99 | 0.9824 | 0.017 | 0.9766 | 0.9825 |
Recall | 0.98 | 0.96 | 1.00 | 0.99 | |||||
F1-Score | 0.99 | 0.97 | 0.98 | 0.99 | |||||
Specificity | 1.00 | 0.99 | 1.00 | 0.99 | |||||
VGG-SCNet | Precision | 0.97 | 0.97 | 0.98 | 0.99 | 0.0226 | 0.9698 | 0.9775 | |
Recall | 0.99 | 0.94 | 0.98 | 0.99 | 0.9774 | ||||
F1-Score | 0.98 | 0.96 | 0.98 | 0.99 | |||||
Specificity | 0.98 | 0.99 | 0.99 | 0.99 | |||||
MEEDNets | Precision | 0.99 | 0.99 | 0.98 | 0.99 | 0.0101 | 0.9833 | 0.9875 | |
Recall | 0.99 | 0.97 | 1.00 | 0.99 | 0.9899 | ||||
F1-Score | 0.99 | 0.98 | 0.99 | 0.99 | |||||
Specificity | 0.99 | 0.99 | 0.99 | 0.99 | |||||
ResGANet | Precision | 0.91 | 0.92 | 0.96 | 0.98 | 0.9425 | 0.057 | 0.9235 | 0.9425 |
Recall | 0.97 | 0.88 | 0.98 | 0.94 | |||||
F1-Score | 0.94 | 0.90 | 0.97 | 0.96 | |||||
Specificity | 0.96 | 0.97 | 0.98 | 0.99 | |||||
Proposed FDEIoL | Precision | 1.00 | 0.99 | 0.99 | 1.00 | 0.99 | 0.01 | 0.97 | 0.995 |
Recall | 1.00 | 0.99 | 1.00 | 0.99 | |||||
F1-Score | 1.00 | 0.99 | 1.00 | 0.99 | |||||
Specificity | 1.00 | 0.99 | 1.00 | 0.99 | |||||
