Exploiting Classifier Combination for Early Melanoma Diagnosis Support View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2000

AUTHORS

E. Blanzieri , C. Eccher , S. Forti , A. Sboner

ABSTRACT

Melanoma is the most dangerous skin cancer and early diagnosis is the main factor for its successful treatment. Experienced dermatologists with specific training make the diagnosis by clinical inspection and they reach 80% level of both sensitivity and specificity. In this paper, we present a multi-classifiers system for supporting the early diagnosis of melanoma. The system acquires a digital image of the skin lesion and extracts a set of geometric and colorimetric features. The diagnosis is performed on the vector of features by integrating with a voting schema the diagnostic outputs of three different classifiers: discriminant analysis, k-nearest neighbor and decision tree. The system is build and validated on a set of 152 skin images acquired via D-ELM. The results are comparable or better of the diagnostic response of a group of expert dermatologists. More... »

PAGES

55-62

References to SciGraph publications

  • 1998-02. Learning in the “Real World” in MACHINE LEARNING
  • 1999-07. Using Correspondence Analysis to Combine Classifiers in MACHINE LEARNING
  • Book

    TITLE

    Machine Learning: ECML 2000

    ISBN

    978-3-540-67602-7
    978-3-540-45164-8

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/3-540-45164-1_7

    DOI

    http://dx.doi.org/10.1007/3-540-45164-1_7

    DIMENSIONS

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