“Estrategia para la detección de tipos de enfermedades oculares usando red neuronal SOM“

• Autores: Huarote Zegarra, Raúl Eduardo, Vega Luján, Yensi, Flores Masías, Edward José, lCuba Aguilar, Cesar Rau, Llanos Chacaltana, Katherine Susan, Larios Franco, Alfredo Cesar, Diaz Reategui, Monica
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Privada Norbert Wiener
• Repositorio: UWIENER-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.uwiener.edu.pe:20.500.13053/7949
• Enlace del recurso: https://hdl.handle.net/20.500.13053/7949; http://dx.doi.org/10.18687/LACCEI2022.1.1.578
• Nivel de acceso: acceso abierto
• Materia: "Strategy, SOM neural network, eyes, glaucoma, diabetic retinopathy."; http://purl.org/pe-repo/ocde/ford#1.02.00

“This research aims to cover a need to be able to classify according to the funds of eyes in diabetic retinopathy disease, how to convert to gray tone, perform an equalization, apply the canny edge highlighting algorithm and apply morphological operations so that a SOM (self-organization ma p ) neural network can be entered and classified. To achieve this, it is classified as 0 to diabetic retinopathy, 1 to glaucoma and 3 to healthy eyes. To corroborate this strategy, a public database of Fundus-images has been taken, being 45 images of eyes for training and for tests 15 images that were not part of the training were used and for the tests 3 images that were not part of the training were used and each grayscale image is scaled to a dimension of 256x256 pixels, managing to demonstrate with this strategy an affectivity of 93.7% certainty in the identification of class of eye disease“