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DOI: 10.14569/IJACSA.2015.060407
PDF

Classification of Ultrasound Kidney Images using PCA and Neural Networks

Author 1: Mariam Wagih Attia
Author 2: F.E.Z. Abou-Chadi
Author 3: Hossam El-Din Moustafa
Author 4: Nagham Mekky

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 6 Issue 4, 2015.

  • Abstract and Keywords
  • How to Cite this Article
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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.

Keywords: Ultrasound kidney images; Feature Extraction; Principal Component Analysis; Neural Network classifier

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}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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