Abstract

Info

Title:: Convolutional neural network-based method for bone age assessment in X-ray image

Author(s):: GU Jing; MA Ruiqi; ZHU Hengan; School of Electronic Engineering, Xian University of Posts and Telecommunications, Xian 710121, China

Keywords:: Keywords: bone age assessment convolutional neural network refining the assessment area grade scoring method TW3-C

Abstract:: Abstract: To solve the problems in traditional methods such as lots of assessment areas in the epiphysis, high dependence of assessment results on doctors and low assessment accuracy, an improved bone age assessment method based on TW3-C method is proposed. According to the characteristics of Chinese childrens bone development, convolutional neural network is used to refine and classify the assessment areas, reducing the traditional 13 bone assessment areas to 10, and the grade scoring method is also improved. The experimental results show that within the error range of 1-year-old, the proposed method improves the accuracy of the predicted value of bone age to 94.42% for men and 93.64% for women. The average absolute error is 0.414 3 years old for men and 0.428 6 years old for women. Compared with that of typical bone age assessment method, the accuracy of the proposed method for bone age assessment is significantly improved.