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Breast Cancer Detection Using Hierarchical B-Spline Networks

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Advances in Natural Computation (ICNC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4221))

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Abstract

In this paper, an optimized hierarchical B-spline network was employed to detect the breast cancel. For evolving a hierarchical B-spline network model, a tree-structure based evolutionary algorithm and the Particle Swarm Optimization (PSO) are used to find an optimal detection model. The performance of proposed method was then compared with Flexible Neural Tree (FNT), Neural Network (NN), and Wavelet Neural Network (WNN) by using the same breast cancer data set. Simulation results show that the obtained hierarchical B-spline network model has a fewer number of variables with reduced number of input features and with the high detection accuracy.

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References

  1. DeSilva, C.J.S., et al.: Artificial Neural networks and Breast Cancer Prognosis. The Australian Computer Journal 26, 78–81 (1994)

    Google Scholar 

  2. Fogel, D.B., Wasson, E.C., Boughton, E.M., Porto, V.W.: A step toward computer-assisted mammography using evolutionary programming and neural networks. Cancer Letters 119(1), 93–97 (1997)

    Article  Google Scholar 

  3. Hulka, B.S., Moorman, P.G.: Breast Cancer: Hormones and Other Risk Factors. Maturitas 38(1), 103–113 (2001)

    Article  Google Scholar 

  4. Chen, Y., Yang, B., Dong, J.: Nonlinear systems modelling via optimal design of neural trees. International Journal of Neural Systems 14, 125–138 (2004)

    Article  Google Scholar 

  5. Chen, Y., Yang, B., Dong, J., Abraham, A.: Time-series forcasting using flexible neural tree model. Information Science 174(3/4), 219–235 (2005)

    Article  MathSciNet  Google Scholar 

  6. Chen, Y., Abraham, A., Yang, B.: Hybrid Neurocomputing for Breast Cancer Detection. In: The Fourth International Workshop on Soft Computing as Transdisciplinary Science and Technology (WSTST 2005), pp. 884–892. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  7. Chen, Y., Yang, B., Dong, J.: Evolving Flexible Neural Networks using Ant Programming and PSO algorithm. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3173, Springer, Heidelberg (2004)

    Google Scholar 

  8. Sastry, K., Goldberg, D.E.: Probabilistic model building and competent genetic programming. In: Riolo, R.L., Worzel, B. (eds.) Genetic Programming Theory and Practise, ch. 13, pp. 205–220. Kluwer, Dordrecht (2003)

    Google Scholar 

  9. Merz, J., Murphy, P.M.: UCI repository of machine learning databases (1996), http://www.ics.uci.edu/-learn/MLRepository.html

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Jinan University, Jinan, 250022, P.R. China

    Yuehui Chen, Mingjun Liu & Bo Yang

Authors
  1. Yuehui Chen
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  2. Mingjun Liu
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  3. Bo Yang
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Editor information

Editors and Affiliations

  1. Life Science Research Center, School of Electronic Engineering, Xidian University, 710071, Xi’an, Shaanxi, China

    Licheng Jiao

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  3. School of Electronic Engineering, Xidian Univ., 710071, Xi’an, P.R. China

    Xin-bo Gao

  4. College of Mathematics and Information Science, Hebei Normal University, 050016, Shijiazhuang, Hebei, P.R. China

    Jing Liu

  5. Multi-Agent Systems Lab,Department of Computer Science, University of Science and Technology of China, 230026, Hefei, China

    Feng Wu

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© 2006 Springer-Verlag Berlin Heidelberg

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Chen, Y., Liu, M., Yang, B. (2006). Breast Cancer Detection Using Hierarchical B-Spline Networks. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_4

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  • DOI: https://doi.org/10.1007/11881070_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45901-9

  • Online ISBN: 978-3-540-45902-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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