Avaliação de Métodos de Classificação de Glaucoma em Imagens do Fundo do Olho

Abstract

Glaucoma is an asymptomatic ocular disease at the onset which, if left untreated, can lead to blindness. The World Health Organization (WHO) has estimated that by 2020 glaucoma should affect 80 million people and by 2040 will be 111.5 million. In this context, the present work aims to compare methods of automatic classification of glaucoma to aid in its diagnosis by medical specialists. For this, two models are developed based on the extraction of characteristics of eye fundus images obtained from the public base RIM-ONE v2, and also uses a simple convolutional neural network (CNN). In the first model, texture characteristics, histogram information and image colors are extracted, which are submitted to the Principal Component Analysis (PCA) to evaluate nine classical classifiers including Multilayers perceptron (MLP) . In the evaluation are used Accuracy, sensitivity, specificity and area under curve (AUC) . The results demonstrate that the best performance is obtained with the MLP classifier with PCA use in the characteristic descriptors, with accuracy of 90.11%, sensitivity 97.44%, specificity of 84.62%, and AUC equal to 0.90.