Bone age assessment model based on multi-dimensional feature fusion using deep learning / 第二军医大学学报

ABSTRACT

Objective To evaluate the bone age of children using deep convolutional neural network based on feature extraction combined with key features and demographic information. Methods Left hand X-ray images were automatically recognized and preprocessed, and then the 17 key region features of bone age in the left hand joint were automatically extracted by X-ray image analysis method based on deep convolutional neural network. The image features of bone age were combined with clinical data (population statistics and gender) to train and test the bone age assessment model. Results The feature region extraction method based on deep learning had better efficiency in extracting feature information than traditional image analysis method, and the feature information combined with clinical information supplemented the information of bone age from another dimension. The average absolute error measured by bone age assessment model based on multidimensional data feature fusion was 0.455, which was better than traditional methods and only end-to-end deep learning method. Conclusion Compared with traditional machine learning methods, the deep convolutional neural network based on feature extraction has better performance, and can improve the predicting accuracy of image-based bone age by combining with population information such as gender and age.