Melanoma recognition by a deep learning convolutional neural network-Performance in different melanoma subtypes and localisations.

Journal: European journal of cancer (Oxford, England : 1990)
Published Date: Jan 20, 2020

Abstract

BACKGROUND: Deep learning convolutional neural networks (CNNs) show great potential for melanoma diagnosis. Melanoma thickness at diagnosis among others depends on melanoma localisation and subtype (e.g. advanced thickness in acrolentiginous or nodular melanomas). The question whether CNN may counterbalance physicians' diagnostic difficulties in these melanomas has not been addressed. We aimed to investigate the diagnostic performance of a CNN with approval for the European market across different melanoma localisations and subtypes.

Authors

  • Julia K Winkler
    Department of Dermatology, University of Heidelberg, Heidelberg, Germany.
  • Katharina Sies
    Department of Dermatology, University of Heidelberg, Heidelberg, Germany.
  • Christine Fink
  • Ferdinand Toberer
  • Alexander Enk
    Department of Dermatology, University of Heidelberg, Heidelberg, Germany.
  • Teresa Deinlein
    Department of Dermatology and Venerology, Medical University of Graz, Graz, Austria.
  • Rainer Hofmann-Wellenhof
    Department of Dermatology, Medical University Graz, Graz, Austria.
  • Luc Thomas
    Department of Dermatology, Centre Hospitalier Lyon Sud, Lyon 1 University, Lyons Cancer Research Center, Lyon, France.
  • Aimilios Lallas
  • Andreas Blum
  • Wilhelm Stolz
    Department of Dermatology, Allergology and Environmental Medicine II, Hospital Thalkirchner Street, Munich, Germany.
  • Mohamed S Abassi
    Faculty of Computer Science and Mathematics, University of Passau, Passau, Germany.
  • Tobias Fuchs
    Department of Research and Development, FotoFinder Systems GmbH, Bad Birnbach, Germany.
  • Albert Rosenberger
    Institute of Genetic Epidemiology at the Center of Statistics, University of Goettingen, Goettingen, Germany.
  • Holger A Haenssle
    Department of Dermatology, University of Heidelberg, Heidelberg, Germany. Electronic address: holger.haenssle@med.uni-heidelberg.de.

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