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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
Bagon, S.: Matlab wrapper for graph cut. (Dec 2006)
Boykov, Y., Veksler, O., Zabih, R.: Efficient approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 20(12), 1222–1239 (2001)
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)
Cavalcanti, P.G., Scharcanski, J.: Automated prescreening of pigmented skin lesions using standard cameras. Comput. Med. Imag. Graph. 35, 481–491 (2011)
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)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley, New York (2001)
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)
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)
Gilmore, S., Hofmann-Wellenhof, R., Soyer, H.: A support vector machine for decision support in melanoma recognition. Exp. Dermatol. 19(9), 830–835 (2010)
Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 147–159 (2004)
Korotkov, K., Garcia, R.: Computerized analysis of pigmented skin lesions: a review. Artif. Intell. Med. 56(2), 69–90 (2012)
Lee, T.K., Claridge, E.: Predictive power of irregular border shapes for malignant melanomas. Skin Res. Technol. 11(1), 1–8 (2005)
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)
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)
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)
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)
Stiglic, G., Kocbek, S., Pernek, I., Kokol, P.: Comprehensive decision tree models in bioinformatics. PLoS ONE 7(3) (2012)
Zhou, Y., Song, Z.: Binary decision trees for melanoma diagnosis. In: 11th International Workshop on Multiple Classifier Systems, Nanjing, 15–17 May 2013
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Zhou, Y., Song, Z. (2014). Melanoma Diagnosis with Multiple Decision Trees. In: Scharcanski, J., Celebi, M. (eds) Computer Vision Techniques for the Diagnosis of Skin Cancer. Series in BioEngineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39608-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-39608-3_10
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39607-6
Online ISBN: 978-3-642-39608-3
eBook Packages: EngineeringEngineering (R0)