Glaucoma is one of the severe visual diseases that lead to damage the eyes irreversibly by affecting the optic nerve fibers and astrocytes. Consequently, the early detection of glaucoma plays a virtual role in the medical field. The literature presents various techniques for the early detection of glaucoma. Among the various techniques, retinal image-based detection plays a major role as it comes under noninvasive methods of detection. While detecting glaucoma disorder using retinal images, various medical features of the eyes, such as retinal nerve fiber layer, cup-to-disc ratio, apex point, optic disc, and optic nerve head, and image features, such as Haralick texture, higher-order spectra, and wavelet energy, are used. In this paper, a review and study were conducted for the different techniques of glaucoma detection using retinal fundus images. Accordingly, 45 research papers were reviewed and the analysis was provided based on the extracted features, classification accuracy, and the usage of different data sets, such as DIARETDB1 data set, MESSIDOR data set, IPN data set, ZEISS data set, local data set, and real data set. Finally, we present the various research issues and solutions that can be useful for the researchers to accomplish further research on glaucoma detection.

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Glaucoma has emerged as the one of the leading causes of blindness. Even though the diagnosis of this disease has not yet been found, the early detection can cure the glaucoma disease. Various works presented for the glaucoma detection have many disadvantages such as increased run time, complex architecture, etc., during the real-time implementations. This work introduces the glaucoma detection system based on the proposed harmonic genetic-based support vector neural network (HG-SVNN) classifier. The proposed system detects glaucoma in the database through four major steps, (1) pre-processing, (2) proposed hybrid feature extraction, (3) segmentation and (4) classification through the proposed HG-SVNN classifier. The proposed model uses both the statistical and the vessel features from the segmented and the pre-processed images to construct the feature vector. The proposed HG-SVNN classifier uses both the harmonic operator and the genetic algorithm (GA) for the neural network training. From the simulation results, it is evident that the proposed glaucoma detection system has better performance than the existing works with the values of 0.945, 0.9, 0.9333 and 0.86667 for the segmentation accuracy, accuracy, sensitivity and specificity metric.

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