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Evaluation metrics for all tested binary classifiers on microscopic blood images.
| Classification Model | Class | Precision | Recall (Sensitivity) | F1-Score | Accuracy |
|---|---|---|---|---|---|
| VGG-16 | ALL | 0.84 | 1.00 | 0.91 | 0.9054 |
| Normal | 1.00 | 0.82 | 0.90 | ||
| ResNet-50 | ALL | 0.90 | 0.97 | 0.93 | 0.9324 |
| Normal | 0.97 | 0.89 | 0.93 | ||
| DenseNet-121 | ALL | 0.95 | 1.00 | 0.97 | 0.9730 |
| Normal | 1.00 | 0.95 | 0.97 | ||
| Developed GAN Classifier | ALL | 0.97 | 1.00 | 0.99 | 0.9865 |
| Normal | 1.00 | 0.97 | 0.99 |
