Abstract: The bone-ages of children and adolescents indicate their growth condition.Traditional clinical method of bone-age assessment is to observe the bone maturity of multiple particular bones inside the X-ray film of the whole left hand by the doctor’s eyes.The assessment accuracy greatly depends on the doctor’s subjective judgment ability,and the evaluation is time-consuming.At present,deep convolution neural network has been used for automated bone-age assessment based on the whole bone image of left hand.In order to improve the accuracy of bone-age assessment,this paper proposed to segment 14 specific bones used for bone-age assessment from each whole hand bone image,and then train a deep convolution neural network (AlexNet) for each one of 14 specific bones to evaluate the bone maturity level.In addition,considering that bone development is a continuous process,unlike selecting some discrete growth-level of bone in the traditional method,this paper uses the classification probabilities of the two most probable levels outputted by the automated neural network to calculate the weighted score.The test results show that the proposed method has the average bone-age error of 0.456 year and has an accuracy of 94.64% when the allowed error range which 1.0 year,which is significantly better than the automated bone-age assessment method based on the whole hand image.

Key words: Accuracy rate, Bone-age assessment, Convolutional neural network, Deep learning