Detection and classification of non-proliferative diabetic retinopathy using a back-propagation neural network

Jesús Salvador Velázquez-González, Alberto Jorge Rosales-Silva, Francisco Javier Gallegos-Funes, Guadalupe De Jesús Guzmán-Bárcenas

Research output: Contribution to journalArticle

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Abstract

One of the most serious complications of type 2 Diabetes Mellitus (DM) is the Diabetic Retinopathy (DR). DR is a silent disease and is only recognized when the changes on the retina have progressed to a level at which treatment turns complicate, so an early diagnosis and referral to an ophthalmologist or optometrist for the management of this disease can prevent 98% of severe visual loss. The aim of this work is to automatically identify Non Diabetic Retinopathy (NDR), and Background Retinopathy using fundus images. Our results show a classification accuracy of 92%, with sensitivity and specifity of 95%.
Original languageAmerican English
Pages (from-to)70-85
Number of pages61
JournalRevista Facultad de Ingenieria
StatePublished - 1 Jan 2015

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