A comparison of machine learning methods for the diagnosis of pigmented skin lesions

Stephan Dreiseitl, Lucila Ohno-Machado, Harald Kittler, Staal Vinterbo, Holger Billhardt, Michael Binder

Research output: Contribution to journalArticlepeer-review

212 Citations (Scopus)


We analyze the discriminatory power of k-nearest neighbors, logistic regression, artificial neural networks (ANNs), decision tress, and support vector machines (SVMs) on the task of classifying pigmented skin lesions as common nevi, dysplastic nevi, or melanoma. Three different classification tasks were used as benchmarks: the dichotomous problem of distinguishing common nevi from dysplastic nevi and melanoma, the dichotomous problem of distinguishing melanoma from common and dysplastic nevi, and the trichotomous problem of correctly distinguishing all three classes. Using ROC analysis to measure the discriminatory power of the methods shows that excellent results for specific classification problems in the domain of pigmented skin lesions can be achieved with machine-learning methods. On both dichotomous and trichotomous tasks, logistic regression, ANNs, and SVMs performed on about the same level, with k-nearest neighbors and decision trees performing worse.

Original languageEnglish
Pages (from-to)28-36
Number of pages9
JournalJournal of Biomedical Informatics
Issue number1
Publication statusPublished - Feb 2001


  • Decision support
  • Image classification
  • Machine learning
  • Neural networks
  • Support vector machines
  • Neural Networks, Computer
  • Diagnosis, Computer-Assisted
  • Humans
  • Logistic Models
  • Melanoma/diagnosis
  • Skin Neoplasms/diagnosis
  • Skin Diseases/classification
  • Algorithms
  • Skin Pigmentation
  • Nevus, Pigmented/diagnosis
  • Decision Trees
  • Nevus/diagnosis


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