Ridge regression neural network for pediatric bone age assessment

Bone age is an important measure for assessing the skeletal and biological maturity of children. Delayed or increased bone a serious concern pediatricians, needs to be accurately assessed in bid determine whether occurring at rate consistent with chronological age. In this paper, we introduce unified deep learning framework assessment using instance segmentation ridge regression. The proposed approach consists two integrated stages. first stage, employ image annotation model annotate segment hand from radiographic image, followed by background removal. second design regression neural network architecture composed pre-trained convolutional salient features segmented pediatric radiographs output layer predicting Experimental evaluation on dataset demonstrates competitive performance our comparison existing based methods assessment.