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

Publication date: Available online 30 April 2019Source: Artificial Intelligence in MedicineAuthor(s): Toan Duc Bui, Jae-Joon Lee, Jitae ShinAbstractBone 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.
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research