FundusQ-Net: A regression quality assessment deep learning algorithm for fundus images quality grading.

Journal: Computer methods and programs in biomedicine
Published Date: May 26, 2023

Abstract

OBJECTIVE: Ophthalmological pathologies such as glaucoma, diabetic retinopathy and age-related macular degeneration are major causes of blindness and vision impairment. There is a need for novel decision support tools that can simplify and speed up the diagnosis of these pathologies. A key step in this process is to automatically estimate the quality of the fundus images to make sure these are interpretable by a human operator or a machine learning model. We present a novel fundus image quality scale and deep learning (DL) model that can estimate fundus image quality relative to this new scale.

Authors

  • Or Abramovich
    The Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel.
  • Hadas Pizem
    Rambam Medical Center: Rambam Health Care Campus, Israel.
  • Jan Van Eijgen
    Research Group of Ophthalmology, Department of Neurosciences, KU Leuven, Oude Markt 13, 3000 Leuven; Department of Ophthalmology, University Hospitals UZ Leuven, Herestraat 49, 3000 Leuven, Belgium.
  • Ilan Oren
    The Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel.
  • Joshua Melamed
    The Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel.
  • Ingeborg Stalmans
    Research Group Ophthalmology, KU Leuven, Kapucijnenvoer 33, 3000 Leuven, Belgium.
  • Eytan Z Blumenthal
    Rambam Medical Center: Rambam Health Care Campus, Israel.
  • Joachim A Behar
    Faculty of Biomedical Engineering, Technion, Israel Institute of Technology, Haifa, Israel.

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