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All Journal International Journal of Electrical and Computer Engineering
Noor Hamzah Alkurdy
Al-Nahrain University
Computer Science & IT Electrical & Electronics Engineering

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Ultrasound renal stone diagnosis based on convolutional neural network and VGG16 features Noor Hamzah Alkurdy; Hadeel K. Aljobouri; Zainab Kassim Wadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3440-3448

Abstract

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.
Co-Authors Hadeel K. Aljobouri Zainab Kassim Wadi