Table 2 Comparison of the effect reference value of two network dental age prediction models
From: A population-based study to assess two convolutional neural networks for dental age estimation
Age range | Network model | Precision (%) | Recall (%) | Accuracy (%) | F1-Score (%) |
|---|---|---|---|---|---|
6–8 | ResNet101-gender | 80.32 | 53.84 | 88.73 | 64.47 |
VGG16 | 83.42 | 82.89 | 93.63 | 83.19 | |
9–11 | ResNet101-gender | 43.57 | 46.42 | 80.06 | 44.95 |
VGG16 | 61.07 | 60.71 | 86.32 | 60.89 | |
12–14 | ResNet101-gender | 53.03 | 28.68 | 75.36 | 37.23 |
VGG16 | 63 | 63.52 | 81.21 | 63.26 | |
15–17 | ResNet101-gender | 31.53 | 48.52 | 72.33 | 38.22 |
VGG16 | 45.54 | 54.43 | 80.48 | 49.59 | |
18–20 | ResNet101-gender | 54.71 | 74.35 | 82.21 | 63.04 |
VGG16 | 71.6 | 59.48 | 86.95 | 64.98 |