A deep learning model for classification of diabetic retinopathy in eye fundus images based on retinal lesion detection

Melissa DelaPava, Hernán Ríos, Francisco J. Rodríguez, Oscar J. Perdomo, Fabio A. González

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Diabetic retinopathy (DR) is the result of a complication of diabetes affecting the retina. It can cause blindness, if left undiagnosed and untreated. An ophthalmologist performs the diagnosis by screening each patient and analyzing the retinal lesions via ocular imaging. In practice, such analysis is time-consuming and cumbersome to perform. This paper presents a model for automatic DR classification on eye fundus images. The approach identifies the main ocular lesions related to DR and subsequently diagnoses the illness. The proposed method follows the same workflow as the clinicians, providing information that can be interpreted clinically to support the prediction. A subset of the kaggle EyePACS and the Messidor-2 datasets, labeled with ocular lesions, is made publicly available. The kaggle EyePACS subset is used as training set and the Messidor-2 as a test set for lesions and DR classification models. For DR diagnosis, our model has an area-under-the-curve, sensitivity, and specificity of 0:948, 0:886, and 0:875, respectively, which competes with state-of-the-art approaches.

Original languageEnglish (US)
Title of host publication17th International Symposium on Medical Information Processing and Analysis
EditorsEduardo Romero, Eduardo Tavares Costa, Jorge Brieva, Leticia Rittner, Marius George Linguraru, Natasha Lepore
ISBN (Electronic)9781510650527
StatePublished - 2021
Event17th International Symposium on Medical Information Processing and Analysis - Campinas, Brazil
Duration: Nov 17 2021Nov 19 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference17th International Symposium on Medical Information Processing and Analysis

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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