Abstract: In this paper, a computer-aided system is proposed for automatic classification of Ultrasound Kidney diseases. Images of five classes: Normal, Cyst, Stone, Tumor and Failure were considered. A set of statistical features and another set of multi-scale wavelet-based features were extracted from the region of interest (ROI) of each image and the principal component analysis was performed to reduce the number of features. The selected features were utilized in the design and training of a neural network classifier. A correct classification rate of 97% has been obtained using the multi-scale wavelet-based features.

Mariam Wagih Attia, F.E.Z. Abou-Chadi, Hossam El-Din Moustafa and Nagham Mekky. “Classification of Ultrasound Kidney Images using PCA and Neural Networks”. International Journal of Advanced Computer Science and Applications (IJACSA) 6.4 (2015). http://dx.doi.org/10.14569/IJACSA.2015.060407

@article{Attia2015,
title = {Classification of Ultrasound Kidney Images using PCA and Neural Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.060407},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060407},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {4},
author = {Mariam Wagih Attia and F.E.Z. Abou-Chadi and Hossam El-Din Moustafa and Nagham Mekky}
}