Comparative analysis of automated bone age assessment techniques

In recent years, automated systems have been implemented in several medical application tasks. For instance, computerized assessment of bone age which utilized by radiologist and pediatrician. This tool is beneficial for monitoring growth condition in children. However, considering the right technique for constructing this computerized bone evaluation is crucial, specifically when enormous data used. In this study, 9 GB X-ray dataset with varied specifications is examined. To address the challenge, we adopt and compare deep learning with several machine learning techniques. ResNet50 which categorized as a deep learning technique is implemented in this study. In contrast, machine learning technique utilizes two keypoints features: Bag of Scale Invariant Feature Transform (SIFT) and Bag of Speeded Up Robust Features (SURF). Bone age is predicted based on those extracted features by several machine learning regression techniques, including Multilayer Perceptron Regressor, Support Vector Regressor, Random Forest Regressor, and XGBoost Regressor. Both techniques are compared quantitatively using three different measurements: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Symmetric Mean Absolute Percentage Error (SMAPE). Experiment results indicate that ResNet50 outperforms overall machine learning techniques. ResNet50 achieves 13.58 months of MAE, 17.14 months of RMSE and 16.81% of SMAPE.