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

Eduardo H.Z.R.; Yensi V.L.; José F.M.E.; Aguilar C.R.C.; Chacaltana K.S.L.; Cesar L.F.A.; Monica D.R. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2022) · DOI: 10.18687/laccei2022.1.1.578

Resumen

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 map) 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.

Instituciones: Universidad Nacional de Trujillo; Universidad Nacional Federico Villarreal; Universidad Nacional Tecnológica de Lima Sur; Universidad Privada Norbert Wiener; Universidad Privada del Norte