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Hybrid approach using KPSO and RLS for RBFNN design for breast cancer detection

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Abstract

Neural networks have been employed in many medical applications including breast cancer classification. Innovation in diagnostic features of tumors may play a central role in development of new treatment methods for earliest stage of breast cancer detection. This study proposes a new hybrid for breast cancer detection by extending the application of a variation of particle swarm optimization called K-particle swarm optimization (KPSO). In this paper, the centers and variances of radial basis functional neural network are initialized by KPSO and then updated using back propagation. The weights are updated using recursive least square instead of back propagation. The results are compared with some recently developed techniques. It is found that the proposed technique provides more accurate result and better classification as compared to some other techniques.

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References

  1. Acevedo ME, Acevedo MA, Felipe F (2009) Classification of cancer recurrence with alpha-beta BAM. Math Probl Eng 2. doi:10.1155/2009/680212

  2. Burke HB, Goodman PH, Rosen DB et al (1997) Artificial neural networks improves the accuracy of cancer survival prediction. Cancer 79(4):857–862

    Article  Google Scholar 

  3. Domingos P (1996) Unifying instance-based and rule-based induction. Mach Learn 24(2):141–168

    Google Scholar 

  4. Boros E, Hammer P, Ibaraki T (2000) An implementation of logical analysis of data. IEEE Trans Knowl Data Eng 12(2):292–306

    Article  MathSciNet  Google Scholar 

  5. Street WN, Kim Y (2001) A streaming ensemble algorithm (SEA) for large-scale classification. In: Proceedings of the 7th ACM SIGKDD international conference on knowledge discovery and data mining (KDD ‘01), ACM, San Francisco, CA, pp 377–382

  6. Wang Y, Witten IH (2002) Modeling for optimal probability prediction. In: Proceedings of the 9th international conference on machine learning (ICML ‘02), pp 650–657

  7. Huang K, Yang H, King I (2004) Biased mini-max probability machine for medical diagnosis. In: Proceedings of the 8th international symposium on artificial intelligence and mathematics (AIM ‘04), Fort Lauderdale, FL

  8. Huang C-L, Liao H-C, Chen M-C (2008) Prediction model building and feature selection with support vector machines in breast cancer diagnosis. Expert Syst Appl 34(1):578–587

    Article  Google Scholar 

  9. John EM (1998) www.zerobreastcancer.org/research/r_marinresearch.html

  10. www.cdc.gov/cancer/breast/statistics/

  11. Jemal A, Murray T, Ward E, Samuels A, Tiwary RC, Ghafoor A, Feuer EJ, Thun MJ (2005) Cancer statistics. CA Cancer J Clin 55:10–30

    Article  Google Scholar 

  12. http://www.cancer.gov/cancertopics/factsheet/detection/probability-breast-cancer

  13. Kaul K, Daguilh FM (2002) Early detection of breast cancer: is mammography enough? R Hosp Phys 9:49–55

    Google Scholar 

  14. Paci E (2002) Mammography and beyond. Developing technologies for the early detection of breast cancer. Breast Cancer Res 4:123–125

    Article  Google Scholar 

  15. Cheng HD, Wu CY, Hung DL (1998) VLSI for moment computation and its application to breast cancer detection. Pattern Recogn 1391–1406

  16. Cheng HD, Shan J, Ju W, Guo Y, Jhang L (2010) Automated breast cancer detection and classification using ultra sound images. A survey. Pattern Recogn 299–317

  17. Wang D, Shi L, Ann Heng P (2009) Automatic detection of breast cancers in mammograms using structured support vector machines. Neuro Comput 72(13–15):3296-3302. doi:10.1016/j.neucom

    Google Scholar 

  18. Ye F, Chen C-Y (2009) Alternative KPSO-clustering algorithm. Tamkang J Sci Eng 12(3):289–298

    MathSciNet  Google Scholar 

  19. McConaghy T, Lung H, Bosse E, Vardan V (2003) Classification of audio radar signals using radial basis function neural network. IEEE Trans Inst Meas 52(6):1771–1779

    Article  Google Scholar 

  20. Samantaray SR, Dash PK, Panda G (2006) Fault classification and location using HS-transform and radial basis function neural network. Electr Power Syst Res. Elsevier Science, pp 897–905. doi:10.1016/j.epsr.2005.11.003

  21. www-jlc.kek.jp/subg/offl/kaltest/doc/kalman.pdf

  22. Liu Y, Qin Z, Shi Z, Lu J (2006) Center particle swarm optimization. Neuro Comput 70(4–6):672–679. doi:10.1016/j.neucom

    Google Scholar 

  23. Hu X, Shi Y, Eberhart R (2004) Recent advances in particle swarm. In: Proceedings of IEEE congress on evolutionary computation, CEC, pp 90–97

  24. Lin C-J, Hsieh M-H (2009) Classification of mental task from EEG data using neural networks based on particle swarm optimization. Neurocomputing 72(4–6):1121–1130. doi:10.1016/j.neucom

    Article  Google Scholar 

  25. Das S, Konar A (2008) Particle swarm optimization and differential evolution, algorithms, analysis and applications. Springer, Berlin

    Google Scholar 

  26. Bhattacharya S, Konar A, Das S, Han SY (2009) A Lyapunov-based extension to particle swarm dynamics for continuous function optimization. Sensors 9(12):9977–9997

    Article  Google Scholar 

  27. Van der Merwe DW, Engelbrecht AP (2003) Data clustering using particle swarm optimization. Evol Comput CEC ‘03 1:215–220. doi:10.1109/CEC.2003.1299577

  28. Chen C-Y, Ye F (2004) Particle swarm optimization algorithm and its application to clustering analysis. IEEE ICNSC 2004, Taipei, Taiwan, pp 789–794

  29. Wu KL, Yang MS (2002) Alternative C-means clustering algorithms. Pattern Recogn 35:2267–2278

    Article  MATH  Google Scholar 

  30. http://ailab.si/orange/doc/datasets/breast-cancer-wisconsin-cont.htm

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Authors and Affiliations

  1. Templecity Institute of Technology and Engineering, Biju Patnaik University of Technology, Rourkela, 752057, India

    M. R. Senapati

  2. Indian Institute of Technology, Bhubaneswar, India

    G. Panda

  3. Sikshya ‘O’ Anusandhan University, Bhubaneswar, 751020, India

    P. K. Dash

Authors
  1. M. R. Senapati
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  2. G. Panda
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  3. P. K. Dash
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Correspondence to M. R. Senapati.

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Senapati, M.R., Panda, G. & Dash, P.K. Hybrid approach using KPSO and RLS for RBFNN design for breast cancer detection. Neural Comput & Applic 24, 745–753 (2014). https://doi.org/10.1007/s00521-012-1286-6

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  • DOI: https://doi.org/10.1007/s00521-012-1286-6

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