Ultrasound renal stone diagnosis based on convolutional neural network and VGG16 features

Noor Hamzah Alkurdy, Hadeel K. Aljobouri, Zainab Kassim Wadi


This paper deals with the classification of the kidneys for renal stones on ultrasound images. Convolutional neural network (CNN) and pre-trained CNN (VGG16) models are used to extract features from ultrasound images. Extreme gradient boosting (XGBoost) classifiers and random forests are used for classification. The features extracted from CNN and VGG16 are used to compare the performance of XGBoost and random forest. An image with normal and renal stones was classified. This work uses 630 real ultrasound images from Al-Diwaniyah General Teaching Hospital (a lithotripsy center) in Iraq. Classifier performance is evaluated using its accuracy, recall, and F1 score. With an accuracy of 99.47%, CNN-XGBoost is the most accurate model.


convolutional neural network; extreme gradient boosting; feature extraction; random forest; VGG16;

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DOI: http://doi.org/10.11591/ijece.v13i3.pp3440-3448

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578