Rough Power Set Tree for Feature Selection and Classification: Case Study on MRI Brain Tumor

  • Waleed Yamany
  • Nashwa El-Bendary
  • Hossam M. Zawbaa
  • Aboul Ella Hassanien
  • Václav Snášel
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 237)


This article presents a feature selection and classification system for 2D brain tumors from Magnetic resonance imaging (MRI) images. The proposed feature selection and classification approach consists of four main phases. Firstly, clustering phase that applies the K-means clustering algorithm on 2D brain tumors slices. Secondly, feature extraction phase that extracts the optimum feature subset via using the brightness and circularity ratio. Thirdly, reduct generation phase that uses rough set based on power set tree algorithm to choose the reduct. Finally, classification phase that applies Multilayer Perceptron Neural Network algorithm on the reduct. Experimental results showed that the proposed classification approach achieved a high recognition rate compared to other classifiers including Naive Bayes, AD-tree and BF-tree.


Rough sets power trees K-mean clustering classification 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Waleed Yamany
    • 1
    • 6
  • Nashwa El-Bendary
    • 2
    • 6
  • Hossam M. Zawbaa
    • 3
    • 6
  • Aboul Ella Hassanien
    • 4
    • 6
  • Václav Snášel
    • 5
  1. 1.Faculty of Computers and InformationFayoum UniversityFayoumEgypt
  2. 2.Arab Academy for Science, Technology, and Maritime TransportCairoEgypt
  3. 3.Faculty of Computers and InformationBeniSuef UniversityBeniSuefEgypt
  4. 4.Faculty of Computers and InformationCairo UniversityCairoEgypt
  5. 5.VSB-Technical University of OstravaOstravaCzech Republic
  6. 6.Scientific Research Group in Egypt (SRGE)GizaEgypt

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