Automatic grading of diabetic retinopathy on a public database

Par Conseil national de recherches du Canada

Auteur	Rechercher : Séoud, Lama; Rechercher : Chelbi, Jihed; Rechercher : Cheriet, Farida
Format	Texte, Article
Conférence	Ophthalmic Medical Image Analysis Second International Workshop, OMIA 2015, held in conjunction with MICCAI 2015, October 9, 2015, Munich, Germany
Résumé	With the growing diabetes epidemic, retina specialists have to examine a tremendous amount of fundus images for the detection and grading of diabetic retinopathy. In this study, we propose a first automatic grading system for diabetic retinopathy. First, a red lesion detection is performed to generate a lesion probability map. The latter is then represented by 35 features combining location, size and probability information, which are finally used for classification. A leave-one-out cross-validation using a random forest is conducted on a public database of 1200 images, to classify the images into 4 grades. The proposed system achieved a classification accuracy of 74.1% and a weighted kappa value of 0.731 indicating a significant agreement with the reference. These preliminary results prove that automatic DR grading is feasible, with a performance comparable to that of human experts.
Date de publication	2015
Dans	Proceedings of the Ophthalmic Medical Image Analysis Second International Workshop (OMIA 2015) : 97–104.
Langue	anglais
Publications évaluées par des pairs	Oui
Publication du CNRC	Cette publication n’est pas du CNRC Ce sont des publications qui ont été rédigées par un auteur du CNRC, mais avant que celui-ci soit employé du CNRC.
Numéro NPARC	23000999
Exporter la notice	Exporter en format RIS
Signaler une correction	Signaler une correction (s'ouvre dans un nouvel onglet)
Identificateur de l’enregistrement	173fd72d-1281-4c07-b26b-35fc38aa8ef5
Enregistrement créé	2016-11-25
Enregistrement modifié	2020-04-22

Date de modification :: 2025-12-05