A pediatric bone age assessment method for hand bone X-ray images based on dual-path network

Bone age assessment is a common diagnostic method used for abnormal growth and development in children. Despite recent significant advancements in convolutional neural network (CNN)-based intelligent bone age assessment in children, there remains room for improvement in assessment accuracy. Studies have indicated that a dual-path network (DPN) can incorporate different features of a CNN and improve the potential of the model to extract critical features compared to a single structural CNN. Attention mechanisms can also contribute to the enhanced ability of the model to extract channel and spatial features. Therefore, we propose a dual attention dual-path network (DADPN) to improve the accuracy of pediatric bone age assessment. DPN serves as a backbone network in DADPN by incorporating residual and dense connections. DPN was modified using two different attention mechanisms while containing gender information to compensate for physiological differences in bone age between males and females. Experiments were performed using this method with the RSNA Pediatric Bone Age Challenge dataset and compared to nine representative bone age assessment methods. This method achieved an optimal assessment accuracy with a mean absolute error (MAE) of 4.76 months. The experimental results suggest that the DADPN can extract the effective features of pediatric hand bone X-ray images and improve bone age assessment accuracy more than other deep learning methods.