CNNs for automatic glaucoma assessment using fundus images: an extensive validation.

Journal: Biomedical engineering online
Published Date: Mar 20, 2019

Abstract

BACKGROUND: Most current algorithms for automatic glaucoma assessment using fundus images rely on handcrafted features based on segmentation, which are affected by the performance of the chosen segmentation method and the extracted features. Among other characteristics, convolutional neural networks (CNNs) are known because of their ability to learn highly discriminative features from raw pixel intensities.

Authors

  • Andres Diaz-Pinto
    Instituto de Investigación e Innovación en Bioingeniería, I3B, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain. andiapin@upv.es.
  • Sandra Morales
    Instituto de Investigación e Innovación en Bioingeniería, I3B, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain.
  • Valery Naranjo
    Instituto de Investigación e Innovación en Bioingeniería, I3B, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain.
  • Thomas Köhler
    Pattern Recognition Lab, University of Erlangen-Nuremberg, Erlangen, Germany.
  • Jose M Mossi
    iTEAM, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain.
  • Amparo Navea
    Instituto de Ciencias Biomédicas, Universidad CEU Cardenal Herrera, Avenida del Seminario s/n, Moncada, 46313, Valencia, Spain.

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