###### Abstract
With regard to the details of their CNN, under the leverage of parameters and accuracy, they chose GoogleNet as their classification model and used transfer learning and data augmentation to enhance the model performance. [PERSON] et al. [9] constructed a bone age assessment system called BoNet, and determined their network architecture by experimenting with combinations of several parameters. Moreover, they introduced the deformation layer, as well as transfer learning, to improve the model performance. Unlike [9], they did not define the classification, but directly regressed the bone age in the output layer. [PERSON] et al. [10] constructed a segmentation system to remove the background, and then used a generative adversarial network to enhance the image. They used Xception as their network architecture and proposed a transfer learning method called paced transfer learning. Furthermore, they used regression to assess bone age. [PERSON] et al. [11] generated multi-scale images, used multiple VGG16 branches to extract multi-scale features and, finally, fused the features of each branch to regress bone age. [PERSON] et al. [12] utilized attention maps to make the model more focused on the important parts of the palm, and then used Inception-v3 to regress bone age. [PERSON] et al. [13] and [PERSON] et al. [14] both used ResNet as their architecture and both utilized classification models, the former of which was classified in one-month intervals, while the latter was classified in one-year intervals. The aforementioned studies all use the entire X-ray film as the input. In order to make the model more focused on key parts during bone age assessment, we will review several ROI-based studies. [PERSON] et al. [15] first assessed fingers and wrists as bROIs, and then used Faster R-CNN to extract 13 ROIs from the bROIs. Each ROI had an independent CNN model (VGG16). Finally, they averaged 13 ROI results for bone age, and each ROI CNN model classified the maturity levels of the ROI, rendering a total of nine maturity stages (A-I). [PERSON] et al. [16] directly added the bone age regression network to Faster R-CNN, and their loss function contained three items: ROI class, coordinates, and bone age value. [PERSON] et al. 's [17] method was similar to the aforementioned research, and used Faster R-CNN to extract eight ROIs. After extracting the ROIs, they trained Inception-v4 separately for different bone sections to classify ROI maturity. There were eight stages of maturity: A-I. Thereafter, they utilized Inception-v4 features to train support vector regression to regress bone age. [PERSON] et al. [18] used Faster R-CNN to extract the ROIs. Then, they used ResNet50 to regress bone age. For each gender, there were six regression networks, with the six models corresponding to six parts, respectively, i.e., distal phalanges, intermediate phalanges, proximal phalanges, carpal bones, radius bones, and ulna bones. [PERSON] et al. (2019) selected 14 ROIs based on the hands and wrists of the Chinese, and used these 14 ROIs to build a CNN model (AlexNet) to regress bone age. For each part, they took the first two highest probability stages to calculate a weighted score, and then the weighted scores of 14 parts to obtain the grade score, followed by mapping the grade score to the bone age comparison table to obtain the bone age. We chose 11 ROIs in our study, as shown in Figure 1.  
As mentioned above, using some specific blocks to assess bone age can eliminate unnecessary blocks before the CNN model makes predictions. In our system, we use 11 bones for bone age assessment: epiphyses in the distal phalanges of the thumb; epiphyses in the intermediate phalanges of the thumb; epiphyses in the distal phalanges of the middle finger; epiphyses in the intermediate phalanges of the middle finger; epiphyses in the proximal phalanges of the middle finger; epiphysis of the distal phalanx of the little finger; epiphysis of the proximal phalanx of the little finger; carpal bones; ulna bones; and radius bones.