Deep Learning Methods for Glaucoma Identification Using Digital Fundus Images

In this survey we analyzed the literature, evaluated the methods for glaucoma identification and identified the main issues faced by other researchers. From the literature it is observed that most of the computer aided diagnosis (CAD) tools for identification of pathological changes in eye fundus are in the early stage of development. The accuracy of glaucoma classification achieved by different methods ranges from 87.50% to 99.41%. However, the classification results are obtained with different data sets and different quality images. Therefore, the further research would be needed to create an algorithm using a data set contained of wider range and various quality images. Also, it is necessary to estimate the advantages and disadvantages of the existing methods and to compare the obtained classification results under the same conditions of experiments.