Methodology for the Identification of Glaucoma in Images of Retina
Abstract
Glaucoma is a disease that damages the optic nerve. the second leading cause of blindness in the world. Several have been proposed. However, these systems were not able to deal with a great diversity of images. Therefore, such methods are not feasible for use in screening programs. We performed an ex- tense study to define the best set of attributes for the representation of image. In total, we evaluated 16,469 characteristics. Our approach to Detection of Glaucoma Uses Texture Descriptors and Neural Networks Convolute- (CNNs). We evaluated our proposal in a total of 873 images of four public databases and concluded that the merging of GLCM and pre- together with the use of the Random Forest classifier are promis- in the detection of this pathology, obtaining an accuracy of 93.35% and an index Kappa considered Excellent.
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