Energetic Glaucoma Segmentation and Classification Strategies using Depth Optimized Machine Learning Strategies

Abstract Glaucoma is a major threatening cause, in which it affects the optical nerve to lead a permanent blindness to individuals. The major causes of Glaucoma are high pressure to eyes, family history, irregular sleeping habits and so on. These kinds of causes leads to Glaucoma easily as well as the affection to such disease leads a heavy damage to the internal optic nervous system and the affected person will get permanent blindness within few months. The eye fluid called aqueous humor is getting blocked inside due to Glaucoma, in normal cases sometimes the fluid comes out from the eye via mesh perspective channel, but this Glaucoma blocks that channel and causes the fluid to getting locked inside and provides the permanent blockage inside. So, that the eyes are getting severe affections such as infection, random blindness in initial stages and so on. The World Health Organization analyzes and reports nearly 80 million people around the globe are affected due to some form of Glaucoma. The major problem with this disease is it is incurable, however, the affection stages can be reduced and maintain the same level of affection as it is for the long period but it is possible only earlier stages of identification. This Glaucoma causes structural affection to the eye ball and it is complex to estimate the cause during regular diagnosis. In medical terms, the Cup to Disc Ratio (CDR) is minimized to the Glaucoma patients suddenly and leads a harmful damage to one's eye in severe manner. The general way to identify the Glaucoma is to take Optical Coherence Tomography (OCT) test, in which it captures the uncovered portion of eye ball (backside) and it is an efficient way to visualize diverse portions of eyes with optical nerve visibility is shown clearly. The OCT images are mainly used to identify the diseases like Glaucoma with proper and robust accuracy levels. In this paper, a new methodology is introduced to identify the Glaucoma on earlier stages called Depth Optimized Machine Learning Strategy (DOMLS), in which it adapts the new optimization logic called Modified K-Means Optimization Logic (MkMOL) to provide best accuracy in results and the proposed approach assures the accuracy level of more than 96.2% with least error rate of 0.002%. This paper focuses on the identification of early stage of Glaucoma and provides an efficient solution to people in case of affection by such disease using OCT images. The exact position point out is handled by using Region of Interest (ROI) based optical region selection, in which it is easy to point the Optical Cup (OC) and Optical Disc (OD). The proposed algorithm of DOMLS proves the accuracy levels in estimation of Glaucoma and shows the practical proofs on resulting section in clear manner.

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