Table 5 Results of the VGG-16 architecture
From: Tongue image quality assessment based on a deep convolutional neural network
| Â | Precision | Recall | F1-score | Accuracy |
|---|---|---|---|---|
Fold 1 | 95.27% | 95.27% | 95.27% | 95.28% |
Fold 2 | 96.50% | 96.43% | 96.47% | 96.46% |
Fold 3 | 95.35% | 95.24% | 95.29% | 95.28% |
Fold 4 | 96.84% | 96.87% | 96.86% | 96.85% |
Fold 5 | 96.85% | 96.85% | 96.85% | 96.85% |
Fold 6 | 98.04% | 98.02% | 98.03% | 98.03% |
Fold 7 | 98.04% | 98.02% | 98.03% | 98.03% |
Fold 8 | 96.09% | 96.04% | 96.07% | 96.06% |
Fold 9 | 98.45% | 98.41% | 98.43% | 98.43% |
Fold 10 | 97.63% | 97.66% | 97.64% | 97.64% |
Average (SD) | 96.91% (1.13%) | 96.88% (1.14%) | 96.89% (1.13%) | 96.89% (1.14%) |