Table 3 Performance comparison among models in bone cancer classification.
Model | Task | Accuracy (%) | Precision | Recall | F1-score | ROC-AUC |
|---|---|---|---|---|---|---|
ResNet50 | Binary classification | 96.8 | 0.97 | 0.96 | 0.97 | 0.98 |
Multi-class | 96.2 | 0.96 | 0.95 | 0.96 | 0.97 | |
EfficientNet-B4 | Binary classification | 97.9 | 0.98 | 0.98 | 0.98 | 0.99 |
Multi-class | 97.3 | 0.97 | 0.97 | 0.97 | 0.98 | |
DenseNet121 | Binary classification | 97.2 | 0.97 | 0.97 | 0.97 | 0.98 |
Multi-class | 96.5 | 0.96 | 0.96 | 0.96 | 0.97 | |
InceptionV3 | Binary classification | 96.5 | 0.96 | 0.96 | 0.96 | 0.97 |
Multi-class | 96.1 | 0.96 | 0.95 | 0.95 | 0.96 | |
VGG16 | Binary classification | 96.0 | 0.96 | 0.96 | 0.96 | 0.96 |
Multi-class | 95.8 | 0.95 | 0.95 | 0.95 | 0.95 |
