Automated classification of infant hip type on ultrasonography using deep learning : preliminary study

Abstract

The purpose of this study is to investigate an effectiveness of a method for automatic classification of infant hip types on ultrasonography. A convolutional neural network（CNN）was adopted for the automated classification of hip types corresponding to the Graf method that was defacto standard method for ultrasonographic assessment of infant hip dysplasia. In the CNN, AlexNet was employed as neural network model. We collected 49 ultrasound images that were classified based on the Graf method by an ultrasonographer. Data augmentation by rotating, mirroring, adjusting contrast, etc., generated additional 246,960 images from the original 49 ones. The augmented images were used as training data of the CNN. The accuracy by 10-fold cross validation was 73%. The CNN would be potentially effective for automatic classification of infant hip types.