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Mammogram Image Classification Using Rough Neural Network

  • K. T. Rajakeerthana
  • C. Velayutham
  • K. Thangavel
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 246)

Abstract

Breast cancer is the second leading cause of cancer deaths in women, and it is the most common type of cancer prevalent among women. Detecting tumor using mammogram is a difficult task because of complexity in the image. This brings the necessity of creating automatic tools to find whether a tumor is present or not. In this paper, rough set theory (RST) is integrated with back-propagation network (BPN) to classify digital mammogram images. Basically, RST is used to handle more uncertain data. Mammogram images are acquired from MIAS database. Artifacts and labels are removed using vertical and horizontal sweeping method. RST has also been used to remove pectoral muscles and segmentation. Features are extracted from the segmented mammogram image using GLCM, GLDM, SRDM, NGLCM, and GLRM. Then, the features are normalized, discretized, and then reduced using RST. After that, the classification is performed using RNN. The experimental results show that the RNN performs better than BPN in terms of classification accuracy.

Keywords

Mammogram BPN Discretization Rough neural network (RNN) Rough set theory 

Notes

Acknowledgments

The third author gratefully acknowledges the UGC, New Delhi, for partial financial assistance under UGC-SAP(DRS) Grant No. F3-50/2011.

References

  1. 1.
    J. Michaelson, S. Satija, and R. Moore, “The pattern of breast cancer screening utilization and its consequences”, vol. 94, no. 1, pp. 37–43, 2002.Google Scholar
  2. 2.
    Wei Pan, “Rough set theory and its application in the intelligent systems”, Proceedings of the 7th World Congress on Intelligent Control and Automation, pp. 3076–3081, 2008.Google Scholar
  3. 3.
    Dongbo Zhang, Yaonan Wang, “Fuzzy-rough neural network and its application to vowel recognition”, Control and Decision, vol. 21, no. 2, pp. 221–224, 2006.Google Scholar
  4. 4.
    Wei Wang, and Hong Mi, “The application of rough neural network in RMF model”, Proceedings of 2nd International Asia Conference on Informatics in Control, Automation and Robotics, pp. 210–213, 2010.Google Scholar
  5. 5.
    Gang Wang, Chenghong Zhang, and Lihua Huang, “A study classification algorithm for data mining based on hybrid intelligent systems”, Proceedings of Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, pp. 371–375, 2008.Google Scholar
  6. 6.
    Chengxi Dong, Dewei Wu, and Jing He, “Decision analysis of combat effectiveness based on rough set neural network”, Proceedings of Fourth International Conference on Natural computation, pp. 227–231, 2008.Google Scholar
  7. 7.
    J F Peters, L Han, and S Ramanna, “Rough neural computing in signal analysis”, Computational Intelligence, vol. 17, no. 3, pp. 493–513, 2001.Google Scholar
  8. 8.
    Dongbo Zhang, “Integrated methods of rough sets and neural network and their applications in pattern recognition”, Hunan university, 2007.Google Scholar
  9. 9.
    Weidong Zhao, and Guohua Chen. “A survey for the integration of rough set theory with neural networks”, Systems engineering and electronics, vol. 24, no. 10, pp. 103–107, 2002.Google Scholar
  10. 10.
    R. M. Haralick, K. Shanmugan, and I. Dinstein, “Textural features for image classification”, IEEE Trans. Syst., Man, Cybern., vol. 3, pp. 610–621, 1973.Google Scholar
  11. 11.
    C. Velayutham, and K. Thangavel, “Unsupervised Quick Reduct Algorithm Using Rough Set Theory”, Journal of Electronic Science and Technology (JEST), vol. 9, no. 3, pp. 193–201, 2011.Google Scholar
  12. 12.
    C. Velayutham, and K. Thangavel, “Entropy Based Unsupervised Feature Selection in Digital Mammogram Image Using Rough Set Theory”, International Journal of Computational Biology and Drug Design, vol. 5, no. 1, pp. 16–34, 2012.Google Scholar
  13. 13.
    K. Thangavel, and C. Velayutham, “Unsupervised Feature Selection in Digital Mammogram Image Using Rough Set Theory”, International Journal of Bioinformatics Research and Applications, vol. 8, no. 5, pp 436–454, 2012.Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  • K. T. Rajakeerthana
    • 1
  • C. Velayutham
    • 2
  • K. Thangavel
    • 3
  1. 1.Department of Electrical and Electronics EngineeringKongu Engineering College PerunduraiErodeIndia
  2. 2.Department of Computer ScienceAditanar College of Arts and Science, VirapandianpatnamTiruchendurIndia
  3. 3.Department of Computer SciencePeriyar UniversitySalemIndia

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