Research Article
BrainNet: Optimal Deep Learning Feature Fusion for Brain Tumor Classification
Table 2
Comparison of prediction accuracy of brain tumors.
| Accuracies % | Classifiers | Org. ResNet101 | Optimized | Feature fusion | %Age difference (org VS FF) | PSO | DE |
| Fine tree | 89.6 | 88.8 | 88 | 92.6 | 3.3% | Linear discriminant | 96 | 95.7 | 95.6 | 95.7 | −0.3% | Cubic SVM | 96.7 | 96.7 | 96.6 | 96.7 | 0% | Boosted trees | 92.8 | 92.1 | 92.3 | 92.5 | −0.3% | Bagged trees | 95.5 | 95.4 | 95.3 | 94.5 | −1% | Subspace discriminant | 95.8 | 95.5 | 95.4 | 95.4 | −0.4% | Narrow neural network | 95.7 | 95.5 | 95.4 | 93.9 | −1.9% | Medium neural network | 96 | 95.8 | 95.8 | 94.4 | −1.7% | Wide neural network | 96.1 | 95.9 | 96.1 | 95.4 | −0.7% |
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