VGG19 demonstrates the highest accuracy rate in a nine-class wound classification task among various deep learning networks: a pilot study.

Journal: Wounds : a compendium of clinical research and practice
Published Date:

Abstract

BACKGROUND: Current literature suggests relatively low accuracy of multi-class wound classification tasks using deep learning networks. Solutions are needed to address the increasing diagnostic burden of wounds on wound care professionals and to aid non-wound care professionals in wound management.

Authors

  • Jun Won Lee
    Department of Plastic and Reconstructive Surgery, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea.
  • Hi-Jin You
    Department of Plastic Surgery, Korea University College of Medicine, Seoul, Republic of Korea.
  • Ji-Hwan Cha
    Department of Plastic and Reconstructive Surgery, Korea University College of Medicine, Seoul, Korea.
  • Tae-Yul Lee
    Department of Plastic and Reconstructive Surgery, Korea University College of Medicine, Seoul, Korea; Institute of Advanced Regeneration and Reconstruction.
  • Deok-Woo Kim
    Department of Plastic and Reconstructive Surgery, Korea University College of Medicine, Seoul, Korea; Institute of Advanced Regeneration and Reconstruction.