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Melanoma Diagnosis with Multiple Decision Trees

Chapter
Part of the Series in BioEngineering book series (SERBIOENG)

Abstract

This chapter highlights the application of multiple binary decision trees in melanoma diagnosis. Since the clinical rules in diagnosing melanoma involve inhomogeneous/non-metric data and various ‘if-then’ statements, direct utilization of machine learning techniques such as neural networks can not perform satisfactorily in modelling the clinical diagnostic knowledge which, typically, is nonlinear and fuzzy. As a versatile and intuitive paradigm in pattern classification, the decision tree is perhaps the optimal mechanism in mimicking the clinical diagnostic rules. This chapter compares the performances of two different designs of the multiple decision trees via experiments. Digital image attributes, including both geometric and colorimetric ones, are all examined in detail. Receiver operating characteristic curves of varying ensemble sizes are presented, illustrating the effectiveness of decision trees in melanoma diagnosis.

Keywords

Computer aided diagnosis Decision tree Melanoma 

References

  1. 1.
    Abbas, Q., Celebi, M.E., Garcia I.F., Rashid M.: Lesion border detection in dermoscopy images using dynamic programming. Skin Res. Technol. 17(1), 91–100 (2011)Google Scholar
  2. 2.
    Alcón, J., Ciuhu, C., Kate, W., Heinrich, A., Uzunbajakava, U., Krekels, G., Siem, D., Haan, G.: Automatic imaging system with decision support for inspection of pigmented skin lesions and melanoma diagnosis. IEEE J. Sel. Top. Signal Process. 3(1), 14–25 (2009)CrossRefGoogle Scholar
  3. 3.
    Bagon, S.: Matlab wrapper for graph cut. (Dec 2006)Google Scholar
  4. 4.
    Boykov, Y., Veksler, O., Zabih, R.: Efficient approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1222–1239 (2001)CrossRefGoogle Scholar
  5. 5.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004)CrossRefGoogle Scholar
  6. 6.
    Cavalcanti, P.G., Scharcanski, J.: Automated prescreening of pigmented skin lesions using standard cameras. Comput. Med. Imag. Graph. 35, 481–491 (2011)CrossRefGoogle Scholar
  7. 7.
    Celebi, M.E., Kingravi, H.A., Uddin, B., Iyatomi, H., Aslandogan, Y.A., Stoecker, W.V., Moss, R.H.: A methodological approach to the classification of dermoscopy images. Comput. Med. Imag. Graph. 31(6), 362–373 (2007)CrossRefGoogle Scholar
  8. 8.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley, New York (2001)Google Scholar
  9. 9.
    Ercal, F., Chawla, A., Stoecker, W., Lee, H., Moss, R.: Neural network diagnosis of malignant melanoma from color images. IEEE Trans. Biomed. Eng. 41(9), 837–845 (1994)CrossRefGoogle Scholar
  10. 10.
    Friedman R, Rigel D, Kopf A.: Early detection of malignant melanoma: the role of physician examination and self-examination of the skin. CA Cancer J. Clin. 35(3), 130–151 (1985)Google Scholar
  11. 11.
    Gilmore, S., Hofmann-Wellenhof, R., Soyer, H.: A support vector machine for decision support in melanoma recognition. Exp. Dermatol. 19(9), 830–835 (2010)CrossRefGoogle Scholar
  12. 12.
    Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 147–159 (2004)CrossRefGoogle Scholar
  13. 13.
    Korotkov, K., Garcia, R.: Computerized analysis of pigmented skin lesions: a review. Artif. Intell. Med. 56(2), 69–90 (2012)CrossRefGoogle Scholar
  14. 14.
    Lee, T.K., Claridge, E.: Predictive power of irregular border shapes for malignant melanomas. Skin Res. Technol. 11(1), 1–8 (2005)CrossRefGoogle Scholar
  15. 15.
    Manousaki, A.G., Manios, A.G., Tsompanaki, E.I., Panayiotides, J.G., Tsiftsis, D.D., Kostaki, A.K., Tosca, A.D.: A simple digital image processing system to aid in melanoma diagnosis in an everyday melanocytic skin lesion unit: a preliminary report. Int. J. Dermatol. 45(4), 402–410 (2006)CrossRefGoogle Scholar
  16. 16.
    Rigel, D., Russak, J., Friedman, R.: The evolution of melanoma diagnosis: 25 years beyond the ABCDs. CA Cancer J. Clin. 60(5), 301–316 (2010)Google Scholar
  17. 17.
    Sboner, A., Eccher, C., Blanzieri, E., Bauer, P., Cristofolini, M., Zumiani, G., et al.: A multiple classifier system for early melanoma diagnosis. Artif. Intell. Med. 27(1), 29–44 (2003)CrossRefGoogle Scholar
  18. 18.
    She, Z., Liu, Y., Damatoa, A.: Combination of features from skin pattern and ABCD analysis for lesion classification. Skin Res. Technol. 13(1), 25–33 (2007)CrossRefGoogle Scholar
  19. 19.
    Stiglic, G., Kocbek, S., Pernek, I., Kokol, P.: Comprehensive decision tree models in bioinformatics. PLoS ONE 7(3) (2012)Google Scholar
  20. 20.
    Zhou, Y., Song, Z.: Binary decision trees for melanoma diagnosis. In: 11th International Workshop on Multiple Classifier Systems, Nanjing, 15–17 May 2013Google Scholar
  21. 21.
    Zhou, Y., Smith, M., Smith, L., Warr, R.: A new method describing border irregularity of pigmented lesions. Skin Res. Technol. 16(1), 66–76 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Computing, Engineering and Physical SciencesUniversity of Central LancashirePrestonUK
  2. 2.Department of Biomedical ScienceUniversity of SheffieldSheffieldUK

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