Bone Age Assessment Based on Deep Convolutional Features and Fast Extreme Learning Machine Algorithm

Bone age is an important metric to monitor children's skeleton development in pediatrics. As the development of deep learning DL-based bone age prediction methods have achieved great success. However, it also faces the issue of huge computation overhead in deep features learning. Aiming at this problem, this paper proposes a new DL-based bone age assessment method based on the Tanner-Whitehouse method. This method extracts limited and useful regions for feature learning, then utilizes deep convolution layers to learn representative features in these interesting regions. Finally, to realize the fast computation speed and feature interaction, this paper proposes to use an extreme learning machine algorithm as the basic architecture in the final bone age assessment study. Experiments based on publicly available data validate the feasibility and effectiveness of the proposed method.