## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **0.05** & **0.1** & **0.15** & **0.2** & **0.25** & **0.3** & **0.35** & **00.4** & **0.45** & **0.5** \\ \hline S\({}_{1}\) & 66.67\% & 66.67\% & 66.67\% & 66.67\% & 66.67\% & 66.67\% & 66.67\% & 75.00\% \\ S\({}_{2}\) & 92.00\% & 93.14\% & 93.71\% & 94.29\% & 94.86\% & 95.43\% & 95.43\% & 97.14\% & 97.71\% \\ S\({}_{3}\) & 66.67\% & 66.67\% & 66.67\% & 66.67\% & 66.67\% & 70.37\% & 74.07\% & 74.07\% & 77.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: th_score on male. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stage** & **Baseline** & **Branch 1** & **Branch 2** & **DIIBAA (th_Score = 0.45)** \\ \hline S\({}_{1}\) & 0.00\% & 50.00\% & 41.67\% & 66.67\% \\ S\({}_{2}\) & 100.00\% & 86.14\% & 95.67\% & 99.13\% \\ S\({}_{3}\) & 0.00\% & 53.84\% & 30.77\% & 61.54\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results on female. data set. We employ these methods on the task of bone age stage identification task and use their staged identification results to input into the corresponding bone age estimation regression network. We compare the application research of these three methods in the medical images. The three methods are: oversampling method, ref. [14] using DCGAN [27] to perform data augmentation for the minority classes, ref. [21] using focal loss [28] to deal with data imbalance problem, ref. [17] is based on transfer learning (ImageNet as the source domain dataset) and data augmentation to deal with the imbalance problem. In the first two methods, we still use densenet169 as the backbone network. In [17], since they pointed out that Xception is the most superior in their method, we use Xception as the backbone network in the method of [17]. Tables 9 and 10 are the comparison of accuracy and MAE, respectively. As shown in Tables 9 and 10, DIIBAA outperforms the other three methods on the problem of this article. We explain these reasons for each method separately. There are two main problems with the DCGAN-based method. First, if the DCGAN cannot be well trained, their help for data augmentation is limited. In addition, as we mentioned earlier, this kind of oversampling method usually just augments the existing data, they are helpful in the expansion of the decision boundary, but since the amount of data in our two stages is too scarce, this method will have ineffective results. For focal loss, it strengthens training on hard samples (equivalent to the minority stages in this article), and minority stages are actually too scarce, so the focal loss is still not enough for training in hard samples. Therefore, its method has a considerable improvement over the baseline network, but still not accurate enough. In the transfer learning-based method, since this method does not directly design data imbalance, it may be more helpful for slightly imbalanced data, but it performs poorly for the more difficult cases of this article. In the comparison of MAE, due to the stage prediction error, the error caused by it is even greater, and DIIBAA is still better than the above three methods because of its error rate. Of course, the MAE of DIIBAA has better results. Finally, we use experiments to explain why we need to classify the mature stages of bone age first, instead of directly using a regression CNN for all stages. We calculate the MAE of the two to compare, and the results are shown in last column of Table 10.  
It can be found that when the bone age is directly regressed without distinguishing stages, the error of their MAE is very large, compared with the first stage. This is because when all stages are mixed training, the range between them is larger and the variation is larger. This makes CNN regression harder to fit. When the stages are separated, each independent network fit is less problematic and therefore more likely to have accurate results.