Glaucoma Identification Using Relevance Vector Machine

Abstract

Glaucoma is a chronic optic neuropathy characterized by curvature of the optic disc and narrowing of the visual field, accompanied by increased intraocular pressure. In glaucoma there will be a weakening of eye function with visual field defects and anatomical damage in the form of excavation (reverberation) and degeneration of the optic nerve pupil which can end in blindness. The glaucoma identification process is done by eye fundus image. The process of identifying glaucoma is still done by the ophthalmologist manually by looking at the fundus image. There are already people who have done this research using the K-NN method, but the level of accuracy is still small and has not measured the processing time for glaucoma identification. Based on this, this study will apply the Relevance Vector Machine method to the glaucoma identification system on the human retina. System performance is measured based on the values of accuracy, precision, recall, and F-Measure. Experiments were carried out on the glaucoma dataset. The average values in terms of accuracy, precision, recall, and f-measure were 80% accuracy, 86% precision, 80% recall, and 79% f-measure, respectively. These values are influenced by the number of datasets from training images, validation images, and test images. Based on these results, the proposed Relevance Vector Machine has an 80% accuracy performance.