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Classification of ultrasound (US) kidney images for diagnosis of congenitalabnormalities of the kidney and urinary tract (CAKUT) in children is achallenging task. It is desirable to improve existing pattern classificationmodels that are built upon conventional image features. In this study, wepropose a transfer learning-based method to extract imaging features from USkidney images in order to improve the CAKUT diagnosis in children.Particularly, a pre-trained deep learning model (imagenet-caffe-alex) isadopted for transfer learning-based feature extraction from 3-channel featuremaps computed from US images, including original images, gradient features, anddistanced transform features. Support vector machine classifiers are then builtupon different sets of features, including the transfer learning features,conventional imaging features, and their combination. Experimental results havedemonstrated that the combination of transfer learning features andconventional imaging features yielded the best classification performance fordistinguishing CAKUT patients from normal controls based on their US kidneyimages.

Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data