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Building of Readable Decision Trees for Automated Melanoma Discrimination

  • Keiichi Ohki
  • M. Emre Celebi
  • Gerald Schaefer
  • Hitoshi IyatomiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)

Abstract

Even expert dermatologists cannot easily diagnose a melanoma, because its appearance is often similar to that of a nevus, in particular in its early stage. For this reason, studies of automated melanoma discrimination using image analysis have been conducted. However, no systematic studies exist that offer grounds for the discrimination result in a readable form. In this paper, we propose an automated melanoma discrimination system that it is capable of providing not only the discrimination results but also their grounds by means of utilizing a Random Forest (RF) technique. Our system was constructed based on a total of 1,148 dermoscopy images (168 melanomas and 980 nevi) and uses only their color features in order to ensure the readability of the grounds for the discrimination results. By virtue of our efficient feature selection procedure, our system provides accurate discrimination results (a sensitivity of 79.8 % and a specificity of 80.7 % with 10-fold cross-validation) under human-oriented limitations and presents the grounds for the results in an intelligible format.

Keywords

Random Forest Feature Selection Method Weak Learner Random Forest Classifier Good Classification Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This research was partially supported by the Ministry of Education, Culture, Science and Technology of Japan (Grant in-Aid for Fundamental research program (C), 26461666, 2014-2017).

References

  1. 1.
    National Cancer Institute: SEER Cancer Statistics Review 1975–2012. NCI, Bethesda, MD (2015)Google Scholar
  2. 2.
    Soyer, H.P., Smolle, J., Kerl, H., Stettner, H.: Early diagnosis of malignant melanoma by surface microscopy. The Lancet 2(8562), 803 (1987)CrossRefGoogle Scholar
  3. 3.
    Stolz, W., Riemann, A., Cognetta, A.B., Pillet, L., Abmayr, W., Holzel, D., et al.: ABCD rule of dermoscopy: a new practical method for early recognition of malignant melanoma. Eur. J. Dermatol. 4(7), 521–527 (1993)Google Scholar
  4. 4.
    Argenziano, G., Fabbrocini, G., Carli, P., Giorgi, V.D., et al.: Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions comparison of the ABCD rule of dermatoscopy and a new 7-point checklist based on pattern analysis. Arch. Dermatol. 134(12), 1563–1570 (1998)Google Scholar
  5. 5.
    Stolz, W., Falco, O.B., Bliek, P., Kandthaler, M., Burgdorf, W.H.C., Cognetta, A.B.: Color Atlas of Dermatoscopy, 2nd enlarged and completely revised edn. Blackwell Publishing, Berlin (2002)Google Scholar
  6. 6.
    Rubegni, P., Cecenini, G., Burroni, M., Perotti, R., Del’Eva, G., Sbano, P., et al.: Automated diagnosis of pigmented skin lesions. Int. J. Cancer 101(6), 576–580 (2002)CrossRefGoogle Scholar
  7. 7.
    Celebi, M.E., Kingravi, H.A., Uddin, B., Iyatomi, H., et al.: A methodological approach to the classification of dermoscopy images. Comput. Med. Imaging Graph. 31(6), 362–373 (2007)CrossRefGoogle Scholar
  8. 8.
    Oka, H., Hashimoto, M., Iyatomi, H., Argenziano, G., Soyer, H.P., Tanaka, M.: Internet-based program for automatic discrimination of dermoscopic images between melanomas and Clark nevi. British J. Dermatol. 150(5), 1041 (2004)CrossRefGoogle Scholar
  9. 9.
    Iyatomi, H., Oka, H., Saito, M., Miyake, A., Kimoto, M., Yamagami, J., et al.: Quantitative assessment of tumor extraction from dermoscopy images and evaluation of computer-based extraction methods for automatic melanoma diagnostic system. Melanoma Res. 16(2), 183–190 (2006)CrossRefGoogle Scholar
  10. 10.
    Iyatomi, H., Oka, H., Celebi, M.E., Ogawa, K., Argenziano, G., Soyer, H.P., Koga, H., Saida, T., Ohara, K., Tanaka, M.: Computer-based classification of dermoscopy images of melanocytic lesions on acral volar skin. J. Invest. Dermatol. 128(8), 2049–2054 (2008)CrossRefGoogle Scholar
  11. 11.
    Iyatomi, H., Oka, H., Celebi, M.E., Tanaka, M., Ogawa, K.: Parameterization of dermoscopic findings for the Internet-based melanoma diagnostic system. In: Proceedings of the IEEE CIISP 2007, pp. 183–193 (2007)Google Scholar
  12. 12.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)zbMATHCrossRefGoogle Scholar
  13. 13.
    Joel, G., Philippe, S.S., et al.: Validation of segmentation techniques for digital dermoscopy. Skin Res. Technol. 8(4), 240–249 (2002)CrossRefGoogle Scholar
  14. 14.
    Celebi, M.E., Iyatomi, H., Schaefer, G., Stoecker, W.V.: Lesion border detection in dermoscopy images. Comput. Med. Imaging Graph. 33(2), 148–153 (2009)CrossRefGoogle Scholar
  15. 15.
    Norton, K.A., Iyatomi, H., Celebi, M.E., Ishizaki, S., et al.: Three-phase general border detection method for dermoscopy images using non-uniform illumination correction. Skin Res. Technol. 18(3), 290–300 (2012)CrossRefGoogle Scholar
  16. 16.
    Hocking, R.R.: The analysis and selection of variables in linear regression. Biometrics 32(1), 1–49 (1976)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Keiichi Ohki
    • 1
  • M. Emre Celebi
    • 2
  • Gerald Schaefer
    • 3
  • Hitoshi Iyatomi
    • 4
    Email author
  1. 1.Graduate School of Science and EngineeringHosei UniversitySaitamaJapan
  2. 2.Department of Computer ScienceLouisiana State University in ShreveportShreveportUSA
  3. 3.Department of Computer ScienceLoughborough UniversityLoughboroughUK
  4. 4.Graduate School of Science and EngineeringHosei UniversityTokyoJapan

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