A superpixel-driven deep learning approach for the analysis of dermatological wounds.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 1, 2020

Abstract

BACKGROUND: The image-based identification of distinct tissues within dermatological wounds enhances patients' care since it requires no intrusive evaluations. This manuscript presents an approach, we named QTDU, that combines deep learning models with superpixel-driven segmentation methods for assessing the quality of tissues from dermatological ulcers.

Authors

  • Gustavo Blanco
    Institute of Mathematics and Computer Sciences, ICMC/USP, Brazil.
  • Agma J M Traina
    Institute of Mathematics and Computer Sciences, ICMC/USP, Brazil. Electronic address: agma@icmc.usp.br.
  • Caetano Traina
    Institute of Mathematics and Computer Science, University of São Paulo, São Paulo, Brazil.
  • Paulo M Azevedo-Marques
    Department of Internal Medicine, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.
  • Ana E S Jorge
    Department of Physical Therapy, DFisio/UFSCar, Brazil.
  • Daniel de Oliveira
    Institute of Computing, IC/UFF, Brazil.
  • Marcos V N Bedo
    Fluminense Northwest Institute, INFES/UFF, Brazil. Electronic address: bedo@icmc.usp.br.

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