Classification of Melanoma Presence and Thickness Based on Computational Image Analysis

  • Javier Sánchez-MonederoEmail author
  • Aurora Sáez
  • María  Pérez-Ortiz
  • Pedro Antonio Gutiérrez
  • Cesar Hervás-Martínez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9648)


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.


Melanoma Feature extraction Dermoscopic image Computer vision Machine learning Multi-class Ordinal classification Imbalanced classification 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Javier Sánchez-Monedero
    • 1
    Email author
  • Aurora Sáez
    • 2
  • María  Pérez-Ortiz
    • 1
  • Pedro Antonio Gutiérrez
    • 3
  • Cesar Hervás-Martínez
    • 3
  1. 1.Department of Quantitative MethodsUniversidad Loyola AndalucíaCórdobaSpain
  2. 2.Signal Theory and Communications DepartmentUniversity of SevilleSevilleSpain
  3. 3.Department of Computer Science and Numerical Analysis, Campus de Rabanales, Edificio Albert EinsteinUniversity of CórdobaCórdobaSpain

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