Strategy for the detection of types of eye diseases using SOM neural network

Authors

Huarote Zegarra, Raúl Eduardo

Vega Lujan, Yensi

Flores Masías, Edward José

Cuba Aguilar, Cesar Raul

Llanos Chacaltana, Susan Katherine

Larios Franco, Alfredo Cesar

Díaz Reátegui, Mónica

Abstract

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 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 3 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.