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In current society, height is one of the conditions for many people to measure the pursuit of perfection. The focus is no more than judging whether the growth plate of the bone is healed or not, and whether it will grow. Generally, the growth plate will heal with age, and the bone will stop growing after the healing is complete. Presently, in clinical bone age analysis, the most famous method is still GP method, which published in 1959 by Greulich and Pyle et al. They used normal left palm and wrist X-ray images to be the references in different ages, and discriminated the difference bone ages between normal person and examinee. However, manual interpretation is a boring task and time-consuming. The faster and accurate automatic estimation for bone age analysis is necessary. This study is based on deep neural network (DNN) algorithm. The Python programming modules, InceptionResNetV2 and Xception, are respectively used to implement ours proposed computer-aided system of bone age estimation. We also apply into the threshold segmentation and major axis correction method to assist the DNN training procedure, which can effectively remove redundant noise around the hand bone in X-ray images. In the experiments, there are 12,611 X-ray images in our database. During threshold segmentation, there are only 14 segmentation fault cases, accounting for 0.1% of total cases. Furthermore, the proposed system with DNN module can obtain a high accuracy rate and a small loss function in the training set. The proposed system in this study effectively enhances the bone age estimation. In the future, different DNN modules can be tried to improve the performance of ours system.
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