Abstract
Bone age assessment is a common method for evaluating the growth and development of children and adolescents. It can be used for diagnosing problems such as rickets and short stature during the growth of adolescents. The traditional bone age assessment method is the doctor’s manual treatment of hand bone X-ray images, and comparing with medical standard pictures to achieve bone age assessment. In order to reduce the workload of doctors in identifying X-ray images and the subjective effect of doctors, the method is proposed by combining deep learning with medical images to implement automatic bone age assessment. Different methods are used to segment the hand bone X-ray images and transfer learning for classification and identification of bone age. The test results show that female bone age test precision were assigned 94.4% within 20 months, male test precision were assigned 90.5% within 20 months.
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