Automatic diagnosis of melanoma using hyperspectral data and GoogLeNet.

Journal: Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
Published Date: Jun 25, 2020

Abstract

BACKGROUND: Melanoma is a type of superficial tumor. As advanced melanoma has a poor prognosis, early detection and therapy are essential to reduce melanoma-related deaths. To that end, there is a need to develop a quantitative method for diagnosing melanoma. This paper reports the development of such a diagnostic system using hyperspectral data (HSD) and a convolutional neural network, which is a type of machine learning.

Authors

  • Ginji Hirano
    Department of Biological System Engineering, Graduate School of Biology-Oriented Science and Technology, Kindai University, Wakayama, Japan.
  • Mitsutaka Nemoto
    The University of Tokyo Hospital.
  • Yuichi Kimura
    Graduate School of Biology-Oriented Science and Technology, Kindai University, Wakayama, Japan. ukimura@ieee.org.
  • Yoshio Kiyohara
    Division of Dermatology, Shizuoka Cancer Center, Shizuoka, Japan.
  • Hiroshi Koga
  • Naoya Yamazaki
    Department of Dermatologic Oncology, National Cancer Center Hospital, Tokyo, Japan.
  • Gustav Christensen
    Department of Dermatology, Lund University, Lund, Sweden.
  • Christian Ingvar
    Department of Dermatology, Lund University, Lund, Sweden.
  • Kari Nielsen
  • Atsushi Nakamura
    Waseda Research Institute for Science and Engineering, Waseda University, Tokyo, Japan.
  • Takayuki Sota
    Department of Electrical Engineering and Bioscience, Waseda University, Tokyo, Japan.
  • Takashi Nagaoka
    Graduate School of Biology-Oriented Science and Technology, Kindai University, Wakayama, Japan.

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