# A Cascade Model with Prior Knowledge for Bone Age Assessment
## 3 Results
### Evaluation of Bone Age Assessment  
**Baseline networks** We pre-trained on five baseline models to select a better performing baseline network for the BAA task, including ResNet50, MobileNet v3, AlexNet, VGG, and MobileNet v2. All baseline networks were trained from scratch, no pre-trained weights were loaded, and the networks were initialized randomly. The MAE assessed by the baseline model and the parametric model size are shown in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Baseline**} & **MobileNet** & **MobileNet** & \multirow{2}{*}{**ResNet50**} & **VGG** & **AlexNet** \\  & **v2** & **v3** & & & \\ \hline MAE & & & & & \\ (month) & 7.21 & 7.73 & 8.49 & 8.77 & 10.23 \\ Para-size (M) & 3.4 & 3.2 & 25 & 138 & 61 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test results for different baseline models. Figure 8: Confusion matrix of RoIs. As observed from Table 2, the MobileNet v2 baseline model has the best evaluation results with the least number of model parameters and, thus, runs efficiently. This is attributed to the fact that the feature extractor of MobileNet v2 is composed of deep-separable convolution, which makes the network light, effectively prevents overfitting of the model, and is suitable for small datasets such as medical datasets. Therefore, MobileNet v2 is chosen as the baseline network for the BAA task in this paper. **Fusion of RoIs after background denoising and RoIs of prior knowledge. ** In order to verify the effectiveness of background denoising and fusion of prior knowledge for the BAA task, we compare the original image (O) and the background denoising hand image (Hand, H) input model. In clinical practice, if the error between the chronological age and bone age is within one year of the ground truth [33], it is considered that the development of this bone is within the normal range. This paper sets three accuracy standards for different ranges of time (that is, 6 months, 12 months, and 24 months) to evaluate the accuracy of the proposed model in a multiscale manner. Above this, the RoIs of RUS-CHN (i.e., Carpal and Phalanx and C and P) multi-channel input networks are compared, and the experimental results are shown in Table 3.  
As shown in Table 3, compared to using \"O\" as input, the effect of \"H\" as the input network was improved and MAE decreased by 0.76 months, and accuracy is improved by 2.04%, 4.1%, and 0.36% for ground truth within \(\pm\)0.5 year, \(\pm\)1 year, and \(\pm\)2 year, respectively. This illustrates that background denoising can improve the accuracy of model evaluation and has a positive effect on BAA model evaluation. In addition, Table 3 shows that the MAE of the model decreases by 1.04 when \"H+C+P\" is used as input compared to \"H\" as input, and accuracy is improved by 2.19%, 9.66%, and 1.45% for ground truth within \(\pm\)0.5 year, \(\pm\)0.5 year, and \(\pm\)0.5 year, respectively. The experimental results in Table 3 illustrate that background denoising and fused prior knowledge can effectively improve the evaluation accuracy of the BAA model and bring a positive impact to the BAA task. **BAA model ablation experiments. ** To evaluate the effectiveness of the improved feature extractor and the proposed bone age assessment label representation for the BAA task, ablation experiments were conducted. Where the improved feature extractor contains two modules for ablation experiments, i.e., CBAM and 5 \(\times\) 5 convolution kernel size (k: 5 \(\times\) 5), \"MP\" in Table 4 represents the proposed Multi-point distribution of bone age labels, and \"G\" denotes the gender information vector. From Table 4, we can see that, compared to the baseline model, applying the improved feature extractor decreases MAE by 1.16 and accuracy is improved by 7.66%, 3.36%, and 0.34% for ground truth within \(\pm\)0.5 year, \(\pm\)1 year, and \(\pm\)2 year, respectively. We believe