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Classification of Melanoma Presence and Thickness Based on Computational Image Analysis

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Book cover Hybrid Artificial Intelligent Systems (HAIS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9648))

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Abstract

Melanoma is a type of cancer that occurs on the skin. Only in the US, 50,000–100,000 patients are yearly diagnosed with melanoma. Five year survival rate highly depends on early detection, varying between 99 % and 15 % depending on the melanoma stage. Melanoma is typically identified with a visual inspection and lately confirmed and classified by a biopsy. In this work, we propose a hybrid system combining features which describe melanoma images together with machine learning models that learn to distinguish melanoma lesions. Although previous works distinguish melanoma and non-melanoma images, those works focus only in the binary case. Opposed to this, we propose to consider finer classification levels within a five class learning problem. We evaluate the performance of several nominal and ordinal classifiers using four performance metrics to provide highlights of several aspects of classification performance, achieving promising results.

This work was partly financed by a grant provided by the TIN2014-54583-C2-1-R project of the Spanish Ministry of Economy and Competitively (MINECO), by FEDER Funds and by the P11-TIC-7508 project of the Junta de Andalucía, Spain.

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Notes

  1. 1.

    https://github.com/ayrna/orca.

  2. 2.

    http://www.uco.es/grupos/ayrna/en/partitions-and-datasets/#paguitierrez2011ieeetnn.

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Author information

Authors and Affiliations

  1. Department of Quantitative Methods, Universidad Loyola Andalucía, Escritor Castilla Aguayo, 4, 14004, Córdoba, Spain

    Javier Sánchez-Monedero &  María Pérez-Ortiz

  2. Signal Theory and Communications Department, University of Seville, 41092, Seville, Spain

    Aurora Sáez

  3. Department of Computer Science and Numerical Analysis, Campus de Rabanales, Edificio Albert Einstein, University of Córdoba, 14071, Córdoba, Spain

    Pedro Antonio Gutiérrez & Cesar Hervás-Martínez

Authors
  1. Javier Sánchez-Monedero
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  2. Aurora Sáez
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  3. María Pérez-Ortiz
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  4. Pedro Antonio Gutiérrez
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  5. Cesar Hervás-Martínez
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Corresponding author

Correspondence to Javier Sánchez-Monedero .

Editor information

Editors and Affiliations

  1. Universidad Pablo de Olavide, Sevilla, Spain

    Francisco Martínez-Álvarez

  2. Universidad Pablo de Olavide, Sevilla, Spain

    Alicia Troncoso

  3. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Sánchez-Monedero, J., Sáez, A., Pérez-Ortiz, ., Gutiérrez, P.A., Hervás-Martínez, C. (2016). Classification of Melanoma Presence and Thickness Based on Computational Image Analysis. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_36

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  • DOI: https://doi.org/10.1007/978-3-319-32034-2_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32033-5

  • Online ISBN: 978-3-319-32034-2

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