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Naive Bayes Learning of Dermoscopy Images

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Artificial Intelligence and Soft Computing (ICAISC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11509))

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Abstract

We show naive Bayes models of the melanoma skin cancer represented by dermoscopy images from two different repositories. The dermoscopy images of each data set are recursively analyzed by the Mallat wavelet tree transform to extract a set of spatio-frequency filters, which are used to build energy based (rotation invariant) features. Such classifiers are built in different wavelet bases and (for one data set) in three image resolutions. Those simple models show varying classification performance, but some wavelet bases are preferable to differentiate between malicious (melanoma) and benign (displastic nevus) lesions and keep it in reduced image resolutions. The presented research contributes to the feature extraction (wrapper) methods.

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References

  1. Tang, Y.Y., Yang, L.H., Liu, J., Ma, H.: Wavelet Theory and Its Application to Pattern Recognition, 2nd edn. World Scientific, Singapore (2009)

    Book  Google Scholar 

  2. Tang, J., Alelyani, S., Liu, H.: Feature Selection for Classification: A Review, Data Classification: Algorithms and Applications, p. 37. CRC Press, Boca Raton (2014)

    MATH  Google Scholar 

  3. Skvara, H., Teban, L., Fiebiger, M., Binder, M., Kittler, H.: Limitations of dermoscopy in the recognition of melanoma. Arch Dermatol. 141, 155–160 (2005)

    Article  Google Scholar 

  4. Goodson, A.G., Grossman, D.: Strategies for early melanoma detection: approaches to the patient with nevi. J. Am. Acad. Dermatol. 60(5), 719–735 (2009)

    Article  Google Scholar 

  5. Oliveira, R.B., Papa, J.P., Pereira, A.S., Tavares, J.M.R.S: Computational methods for pigmented skin lesion classification in images: review and future trends, neural computing and applications (2016)

    Google Scholar 

  6. Korotkov, K., Garcia, R.: Computerized analysis of pigmented skin lesions: a review. Artif. Intell. Med. 56(2), 69–90 (2012)

    Article  Google Scholar 

  7. Masood, A., Al-Jumaily, A.: Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int. J. Biomed. Imaging 2013(7), 323268 (2013)

    Google Scholar 

  8. Kittler, H., Pehamberger, H., Wolff, K., Binder, M.: Follow-up of melanocytic skin lesions with digital epiluminescence microscopy: patterns of modifications observed in early melanoma, atypical nevi, and common nevi. J. Am. Acad. Dermatol. 43(3), 467–476 (2000)

    Article  Google Scholar 

  9. Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.A. (eds.): Feature extractions: Foundations and Applications. Springer, Heidelberg (2006). https://doi.org/10.1007/978-3-540-35488-8

    Book  Google Scholar 

  10. Mendonca, T., Ferreira, P.M., Marques, J.S., Marcal, A.R., Rozeira, J.: PH2-A dermoscopic image database for research and benchmarking. In: 35th International Conference on IEEE Engineering in Medicine and Biology Society, pp. 5437–5440 (2013)

    Google Scholar 

  11. Rastgoo, M., et al.: Tackling the problem of data imbalancing for melanoma classification. In: BIOSTEC - 3rd International Conference on BIOIMAGING (2016)

    Google Scholar 

  12. Daubechies, I.: Ten Lectures on Wavelets. In: CBMS, vol. 61. SIAM (1994)

    Google Scholar 

  13. Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)

    Article  Google Scholar 

  14. Surówka, G., Merkwirth, C., Żabińska-Płazak, E., Graca, A.: Wavelet based classification of skin lesion images. Bio Alg. Med Syst. 2(4), 43–49 (2006)

    Google Scholar 

  15. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, 2nd edn. Springer, Heidelberg (2008). https://doi.org/10.1007/978-0-387-84858-7

    Book  MATH  Google Scholar 

  16. Manning, C.D., Raghavan, P., Schütze, M.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)

    Book  Google Scholar 

  17. MATLAB: The MathWorks Inc., USA, release: 2018B (1994–2018)

    Google Scholar 

  18. Maglogiannis, I., Doukas, C.N.: Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans. Inf. Tech. Biomed. 13(5), 721–733 (2009)

    Article  Google Scholar 

  19. Surówka, G., Ogorzałek, M.: On optimal wavelet bases for classification of melanoma images through ensemble learning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS (LNAI), vol. 9692, pp. 655–666. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39378-0_56

    Chapter  Google Scholar 

  20. Surówka, G.: Search for resolution invariant wavelet features of melanoma learned by a limited ANN classifier. Schedae Informaticae 25, 189–207 (2016)

    Google Scholar 

  21. Surówka, G., Ogorzałek, M.: Resolution invariant wavelet features of melanoma studied by SVM classifiers. PLOS One (2019). https://doi.org/10.1371/journal.pone.0211318

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, 30-151, Kraków, Poland

    Grzegorz Surówka & Maciej Ogorzałek

Authors
  1. Grzegorz Surówka
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  2. Maciej Ogorzałek
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Corresponding author

Correspondence to Grzegorz Surówka .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Surówka, G., Ogorzałek, M. (2019). Naive Bayes Learning of Dermoscopy Images. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_27

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  • DOI: https://doi.org/10.1007/978-3-030-20915-5_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20914-8

  • Online ISBN: 978-3-030-20915-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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