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

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