Blood vessel detection using enhanced DeepJoint fuzzy clustering algorithm with deep Maxout network for glaucoma detection

Abstract

SummaryGlaucoma became a leading reason for losing vision as it progresses in a gradual manner without any symptoms. The discovery of glaucoma at an earlier phase is imperative as it can help to accelerate the progress. The fundus images are an option for screening glaucoma and help to enable the observation of optic disc. The classical techniques are less accurate and time‐consuming. In this way, a highly accurate automatic glaucoma diagnosis is developed in this research. The noise in the images is eliminated during pre‐processing. Additionally, the DeepJoint model and improved fuzzy clustering technique are combined to develop the proposed Enhanced DeepJoint fuzzy clustering algorithm, which is used to segment blood vessels. The optic disc is additionally divided using black hole entropic fuzzy clustering (BHEFC). In order to identify glaucoma, the acquired segments are fed into a deep Maxout network that has been trained using the multi‐verse water rider optimization (MVRWO) algorithm. By combining the rider optimization algorithm, multi‐verse optimizer, and water wave optimization (WWO), the MVRWO is produced (ROA). The developed model has the greatest accuracy, sensitivity, and specificity scores, coming in at 93.91%, 95.61%, and 92.57% respectively.