  Classification of ultrasound (US) kidney images for diagnosis of congenital
abnormalities of the kidney and urinary tract (CAKUT) in children is a
challenging task. It is desirable to improve existing pattern classification
models that are built upon conventional image features. In this study, we
propose a transfer learning-based method to extract imaging features from US
kidney images in order to improve the CAKUT diagnosis in children.
Particularly, a pre-trained deep learning model (imagenet-caffe-alex) is
adopted for transfer learning-based feature extraction from 3-channel feature
maps computed from US images, including original images, gradient features, and
distanced transform features. Support vector machine classifiers are then built
upon different sets of features, including the transfer learning features,
conventional imaging features, and their combination. Experimental results have
demonstrated that the combination of transfer learning features and
conventional imaging features yielded the best classification performance for
distinguishing CAKUT patients from normal controls based on their US kidney
images.
