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Breast Cancer Detection and Classification Using Support Vector Machines and Pulse Coupled Neural Network

  • Aboul Ella Hassanien
  • Nashwa El-Bendary
  • Miloš Kudělka
  • Václav Snášel
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 179)

Abstract

This article introduces a hybrid scheme that combines the advantages of pulse coupled neural networks (PCNNs) and support vector machine, in conjunction with type-II fuzzy sets and wavelet to enhance the contrast of the original images and feature extraction. An application of MRI breast cancer imaging has been chosen and hybridization scheme have been applied to see their ability and accuracy to classify the breast cancer images into two outcomes: cancer or non-cancer. In order to enhance the contrast of the input image, identify the region of interest and detect the boundary of the breast pattern, a type-II fuzzy-based enhancement and PCNN-based segmentation were applied. Finally, wavelet-based features are extracted and normalized and a support vector machine classifier were employed to evaluate the ability of the lesion descriptors for discrimination of different regions of interest to determine whether they represent cancer or not. To evaluate the performance of presented approach, we present tests on different breast MRI images.

Keywords

Support Vector Machine Magnetic Resonance Imaging Image Magnetic Resonance Imaging Breast Pulse Couple Neural Network Breast Cancer Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work has been supported by Cairo University, project Bio-inspired Technology in Women Breast Cancer Classification, Prediction and Visualization.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Aboul Ella Hassanien
    • 1
    • 2
  • Nashwa El-Bendary
    • 3
    • 4
    • 5
  • Miloš Kudělka
    • 6
  • Václav Snášel
    • 6
  1. 1.Faculty of Computers and Information, Blind Center of TechnologyCairo UniversityCairoEgypt
  2. 2.ABO Research LaboratoryCairoEgypt
  3. 3.Arab Academy for Science, Technology, and Maritime TransportCairoEgypt
  4. 4.ABO Research LaboratoryCairoEgypt
  5. 5.VSB-Technical University of OstravaOstravaCzech Republic
  6. 6.Faculty of Electrical Engendering and Computer ScienceVSB-Technical University of OstravaOstravaCzech Republic

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