Neural Computing and Applications

, Volume 22, Issue 7–8, pp 1591–1598 | Cite as

Local linear wavelet neural network based breast tumor classification using firefly algorithm

Original Article
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Abstract

Breast cancer is the major cause of cancer deaths in women today and it is the most common type of cancer in women. This paper presents some experiments for classifying breast cancer tumor and proposes the use of firefly algorithm (FA) to improve the performance of Local linear wavelet neural network. This work in fact uses FA to optimize the parameters of local linear wavelet neural network. The experiments were conducted on extracted breast cancer data from University of Winconsin Hospital, Madison. The result has been compared with a wide range of classifiers to evaluate its performance. The evaluations show that the proposed approach is very robust, effective and gives better correct classification as compared to other classifiers.

Keywords

Local linear wavelet neural network (LLWNN) Firefly algorithm (FA) Pattern recognition Wisconsin breast cancer (WBC) Minimum description length (MDL) 
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References

  1. 1.
    Antonie M-L, Zaine OR, Coman A (2001) Application of data mining techniques for medical image classification. In: Proceedings in the 2nd international workshop on multimedia data mining, (MDM/KDD’2001), San Francisco, USA, August 26th, pp 94–101Google Scholar
  2. 2.
    Lai S, Li X, Bischof W (1989) On techniques for detecting circumscribed masses in mammograms. IEEE Trans Med Imaging 8(4):377–386CrossRefGoogle Scholar
  3. 3.
    Liu B, Cheng HD, Huang J, Tian J, Tang X, Lie J (2010) Fully Automatic and segmentation—robust classification of breast tumors based on local texture analysis of ultra sound images. Pattern Recognit 43:280–298MATHCrossRefGoogle Scholar
  4. 4.
    Cheng HD, Shan J, Ju W, Guo Y, Jhang L (2010) Automated breast cancer detection and classification using ultra sound images, a survey. Pattern Recognit 43:299–317MATHCrossRefGoogle Scholar
  5. 5.
    Wang T, Narayannis N (1998) Detection of micro calcification in digital mammograms using Wavelets. IEEE Trans Med Imaging 17(4):498–509CrossRefGoogle Scholar
  6. 6.
    Christoyianni I et al (2000) Fast detection of masses in computer-aided mammography. IEEE Signal Process Mag 54–64Google Scholar
  7. 7.
    Chen C, Lee G (1997) Image segmentation using multi resolution wavelet analysis and expectation maximization (em) algorithm for digital mammography. Int J Image Syst Technol 8(5):491–504CrossRefGoogle Scholar
  8. 8.
    Zou W, Chi Z, Lo KC (2008) Improvement of image classification using wavelet coefficients with structured-based neural network. Int J Neural Syst 18(3):195–205. doi: 10.1142/S012906570800152x CrossRefGoogle Scholar
  9. 9.
    Li H et al (1997) Fractal modeling and segmentation for the enhancement of micro calcification in digital mammograms. IEEE Trans Med Imaging 16(6):785–798CrossRefGoogle Scholar
  10. 10.
    Chan H et al (1998) Computerized analysis of mammographic micro calcification in morphological and feature spaces. Med Phys 25(10):2007–2019CrossRefGoogle Scholar
  11. 11.
    Downs J, Harrison RF, Cross SS (1998) A decision support tool for the diagnosis of breast cancer based upon Fuzzy ARTMAP. Neural Comput Appl 7(2):147–165. doi: 10.1007/BF01414167 Google Scholar
  12. 12.
    Li H et al (1995) Markov random field for tumor detection in digital mammography. IEEE Trans Med Imaging 14(3):565–576CrossRefGoogle Scholar
  13. 13.
    Ripley RM, Harris AL, Tarassenko L (1998) Neural network models for breast cancer prognosis. Neural Comput Appl 7(4):367–375. doi: 10.1007/BF01428127 Google Scholar
  14. 14.
    Huang Y-L, Wang K-L, Chen D-R (2006) Diagnosis of breast tumors with ultrasonic texture analysis using support vector machines. Neural Comput Appl 15(2):164–169. doi: 10.1007/s00521-005-0019-5 Google Scholar
  15. 15.
    Chen Y, Yang B, Dong J (2006) Time series prediction using a local linear wavelet neural network. Neuro Comput 69:449–465Google Scholar
  16. 16.
    Chen Y, Dong J, Yang B, Zhang Y (2004) Local linear wavelet neural network. Fifth world congress on intelligent control and automation (WCIA), HangzhouGoogle Scholar
  17. 17.
    Yang XS (2008) Nature-inspired meta heuristic algorithms. Luniver PressGoogle Scholar
  18. 18.
    El-Sebakhy E (2004) Functional networks as a new frame work for the pattern classification problems. Ph.D Thesis, Cornell University, USAGoogle Scholar
  19. 19.
    El-Sebakhy EA, Faisal KA, Helmy T, Azzedin F, Al-Suhaim A (2006) Evaluation of breast cancer tumor classification with unconstrained functional networks classifier. IEEE Int Conf Comput Syst Appl 281–287. doi: 10.1109/AICCSA.2006.205102
  20. 20.
    Karabatak M, Ince MC (2009) An expert system for detection of breast cancer based on association rules and neural network. Expert Syst Appl 36((2, Part 2)):3465–3469CrossRefGoogle Scholar
  21. 21.
    Peña-Reyes CA, Sipper M (1999) A fuzzy-genetic approach to breast cancer diagnosis. Artif Intell Med 17(2):131–155CrossRefGoogle Scholar
  22. 22.
    Abonyi J, Szeifert F (2003) Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recognit Lett 24(14):2195–2207MATHCrossRefGoogle Scholar
  23. 23.
    Guijarro-Berdiñas B, Fontenla-Romero O, Pérez-Sánchez B, Praguela F (2007) A linear learning method for multilayer perceptrons using least-squares. In: Intelligent data engineering and automated learning—IDEAL, vol 4881. Lecture notes in computer science. Springer, Berlin, pp 365–374. doi: 10.1007/978-3-540-77226-2_38
  24. 24.
    Senapati MR, Mohanty A, Dash S, Dash PK (2011) Local linear wavelet neural network for breast cancer recognition. Neural Comput Appl. doi: 10.1007/s00521-011-0670-y
  25. 25.
    Mat A, Harsa S, Nuryanti MS, Nor HO (2009) Neural network inputs selection for breast cancer cells classification, studies in computational intelligence, vol 199, Springer, pp 1–11. doi: 10.1007/978-3-642-00909-9_1
  26. 26.
    Ster B, Dobnikar A (1996) Neural network in medical diagnosis: comparison with other methods, EANN’96, pp 427–430Google Scholar
  27. 27.
    Kordos M, Blachnik M, Strzempa D (2010) Do we need more than k-NN? Artif Intell Soft Comput Lect Notes Soft Comput 6113:414–421. doi: 10.1007/978-3-642-13208-7_52 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Krupajal Engineering CollegeBiju Patnaik University of TechnologyRourkelaIndia
  2. 2.S ‘O’ A UniversityBhubaneswarIndia

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