Fig. 9.

Performance of four different methods (M1–M4) of training for female (a) and male (b) bone age assessments. M1 trains a CNN from scratch with a random weight initialization on original images down sampled to 224 × 224 pixels. M2 contains images from the automated preprocessing engine. M3 contains synthetically generated images for improving network generalization in addition to M2. M4 fine-tunes an ImageNet pretrained CNN on the preprocessed images with data augmentation turned on. “Correct” corresponds to the case where the prediction of the model is the same as the ground truth. “Within 1 year” and “within 2 years” include the cases where the network’s prediction is within 1 and 2 years, respectively. In addition, root mean squared error (RMSE) and mean average precision (mAP) were reported for the four different models to figure out how robust and well-performing each model is