Convolutional Neural Network and Attention Mechanism for Bone Age Prediction Deep learning has expanded essential role in healthcare system for monitoring and diagnosis support. Rather than trying to make any diagnostic decision, the system just acting as existing medical wisdom accessible. Therefore, this research aims to apply deep learning technique, convolutional neural networks (CNN) for bone age classification as a supporting tool for related fields in bone age diagnosis. Although there are various types of researches have been investigated for bone age assessment with CNN, the Attention mechanism has not been profoundly compared with standardized atlas collection of hand radiography for bone age assessment. Therefore, we propose the investigation of bone age prediction model based on Deep Learning techniques and Attention mechanism for screening tool in bone age evaluation.

Bone age assessment using general use convolutional neural networks Deep Learning methods have been applied to different medical imaging analysis tasks like, e.g., lesion classification and tissue segmentation. Bone age assessment is traditionally performed on an xray of the nondominant hand applying the Greulich and Pyle or the Tanner Whitehouse methods. In this work, we first have tested several stateoftheart Convolution Neural Networks models for assessing bone age that previously has shown great results in general computer vision tasks. Based on these results, we have developedoptimized a new model, which is presented here. For this purpose, we used transfer learning methods and trained the selected networks from scratch achieving a 7.89month error rate when assessing bone age in females.