Convolutional neural network and texture descriptor-based automatic detection and diagnosis of glaucoma

Glaucoma is an asymptomatic disease caused by damage to the optic nerve due to elevated intraocular pressure. With early diagnosis, the chances of controlling progression are greater. Glaucoma is a major global health problem as a cause of blindness, second only to cataracts. This paper presents the development of a method for the automatic detection of glaucoma in retinal images using a deep learning approach together with the exploration of texture attributes through phylogenetic diversity indexes. The methodology employed is as follows: First, image acquisition is done from the RIM-ONE, DRIONS-DB, and DRISHTI-GS databases, followed by training the convolutional neural network for optical disk segmentation. After this segmentation, it is necessary to perform removal of the blood vessels after which feature extraction is applied to the images generated from the RGB channels and the gray levels. The extracted attributes are based only on the features of texture using phylogenetic diversity indexes. Classification was performed using an approach based on the convolutional neural network. The method proved to be promising, achieving accuracy, sensitivity, and specificity of 100%. The medical expertise developed by the proposed method proved to be robust and efficient for the automatic detection task of glaucoma through retinal image analysis, using image processing and computational intelligence techniques. We thus propose a specialist system through the construction of a complete CAD tool to integrate into real clinical environments, providing a second opinion on the diagnosis of glaucoma quickly, efficiently, and at low cost. (C) 2018 Elsevier Ltd. All rights reserved.