Incorporated region detection and classification using deep convolutional networks for bone age assessment

•The first attempt to integrate TW3 and CNN-based method based on deep learning.•Explore the expert knowledge from TW3 for bone age assessment by deep convolution networks.•Incorporation of TW3 scheme with deep learning shows better performance than the GPbased method.•Achieves a mean absolute error of about 0.59 years between manual radiology expert and automatic evaluation. Bone age assessment plays an important role in the endocrinology and genetic investigation of patients. In this paper, we proposed a deep learning-based approach for bone age assessment by integration of the Tanner-Whitehouse (TW3) methods and deep convolution networks based on extracted regions of interest (ROI)-detection and classification using Faster-RCNN and Inception-v4 networks, respectively. The proposed method allows exploration of expert knowledge from TW3 and features engineering from deep convolution networks to enhance the accuracy of bone age assessment. The experimental results showed a mean absolute error of about 0.59 years between expert radiologists and the proposed method, which is the best performance among state-of-the-art methods.