@inproceedings{d9f4c4e2bbd446abb9efc51a1165500e,
title = "Combining morphometric features and convolutional networks fusion for glaucoma diagnosis",
abstract = "Glaucoma is an eye condition that leads to loss of vision and blindness. Ophthalmoscopy exam evaluates the shape, color and proportion between the optic disc and physiologic cup, but the lack of agreement among experts is still the main diagnosis problem. The application of deep convolutional neural networks combined with automatic extraction of features such as: the cup-to-disc distance in the four quadrants, the perimeter, area, eccentricity, the major radio, the minor radio in optic disc and cup, in addition to all the ratios among the previous parameters may help with a better automatic grading of glaucoma. This paper presents a strategy to merge morphological features and deep convolutional neural networks as a novel methodology to support the glaucoma diagnosis in eye fundus images.",
author = "Oscar Perdomo and John Arevalo and Gonz{\'a}lez, {Fabio A.}",
note = "Funding Information: Oscar Perdomo and John Ar{\'e}valo thank COLCIENCIAS for funding this research with a doctoral grant. Publisher Copyright: {\textcopyright} 2017 SPIE. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.; 13th International Conference on Medical Information Processing and Analysis, SIPAIM 2017 ; Conference date: 05-10-2017 Through 07-10-2017",
year = "2017",
doi = "10.1117/12.2285964",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Natasha Lepore and Jorge Brieva and Garcia, {Juan David} and Eduardo Romero",
booktitle = "13th International Conference on Medical Information Processing and Analysis",
address = "United States",
}