Abstract
Many eye disorders such as glaucoma, diabetic retinopathy, age-related macular degeneration, high blood pressure, and cataracts can be detected by analyzing retinal fundus images. Deep learning methods with image processing techniques are used for these types of diagnosis systems. In this section, deep learning methods are combined with image processing techniques to detect glaucoma in color fundus images. To emphasize the importance of selecting the proper image processing technique, sample applications are given with both threshold segmentation and morphologic image gradient methods. A classical convolutional neural network is used as a deep learning method. Performance evaluations of the proposed methods were made using Drishti-GS and high-resolution fundus retinal fundus images sets. The results are better with the morphologic image gradient method compared with the threshold segmentation method.
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