Hamate classification method based on feature‐enhanced residual network and probabilistic joint judgment

Accurate analysis of the maturity grade of the reference bone in the wrist is critical for bone age assessment. The main difference between the maturity grades of the reference bone in the wrist X-ray image is the difference in the texture and morphological characteristics of the bone, and the difference in the characteristics between adjacent levels is small, which brings great challenges in bone age assessment. The hamate (the wrist bone in line with the 4th and 5th fingers) is one of the most important reference bones in the standards of skeletal maturity of the hand and wrist for the Chinese (CHN) method. Aiming at the problem of hamate maturity level evaluation, a method of hamate maturity level classification based on feature enhanced residual network and probability joint judgment is proposed. In this method, we propose the enhanced characteristic residual network (ECR-Net) is proposed to enhance the feature extraction capability of the network and improve the loss function to reduce the impact of cross-grade errors on the accuracy of bone age assessment. On this basis, multiple convolutional neural networks to make joint probabilistic judgments are relied on to obtain the final hamate maturity grade. The proposed ECR-Net achieves a macro accuracy of 96.92% on the hamate maturity evaluation, and there are no errors across two levels. Also, this approach achieves a macro accuracy of 97.15% when the probabilistic joint judgment method is employed while avoiding errors that span more than two levels. The network model and method proposed in this paper have good accuracy and practicability for the automatic identification of the hamate maturity level, which is of great significance for the accurate assessment of bone age.