TY - JOUR
T1 - Detection and classification of non-proliferative diabetic retinopathy using a back-propagation neural network
AU - Velázquez-González, Jesús Salvador
AU - Rosales-Silva, Alberto Jorge
AU - Gallegos-Funes, Francisco Javier
AU - De Jesús Guzmán-Bárcenas, Guadalupe
PY - 2015
Y1 - 2015
N2 - 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%.
AB - 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%.
KW - Automatically identification
KW - Diabetic retinopathy
KW - Early diagnosis
KW - Fundus images
UR - http://www.scopus.com/inward/record.url?scp=84929461855&partnerID=8YFLogxK
M3 - Artículo
SN - 0120-6230
VL - 1
SP - 70
EP - 85
JO - Revista Facultad de Ingenieria
JF - Revista Facultad de Ingenieria
IS - 74
ER -