Early detection of glaucoma using optic disc and optic cup segmentation: A survey. (2021)

• Record Type: Journal Article
• Authors: Janani, R.; Rajamohana, S.P.
• Is Part Of: Materials today. Volume 45:Part 2(2021)
• Page Start: 2763
• Page End: 2769
• Publication Date: 2021
• Subjects: Convolution neural Network -- Deep learning -- Glaucoma detection -- Machine learning -- Visual impairment -- Transfer Learning; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2020.11.613
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Glaucoma is the subsequent driving reason for visual impairment around the world. Glaucoma happens because of number of issues in eye which brings about continuous visual misfortune. A portion of the glaucoma screenings are either unidentified or untreated. Glaucoma is recognized by additional liquid in the front of eye. A review of automated glaucoma detection techniques is presented in this paper. Two significant classes of glaucoma are open-edge glaucoma and limited point glaucoma. Manifestations acquired by intense edge conclusion glaucoma are foggy vision, radiances around lights, extraordinary eye torment, queasiness and retching. A portion of the hazard elements of glaucoma are diabetes, hypertension or family ancestry of glaucoma. Decrease or disposal of undiscovered glaucoma is made by starting mindfulness and continuous assessments by Ophthalmologist. A detailed survey is performed on early analysis of glaucoma utilizing different techniques such as Machine Learning, Convolution Neural Network, Transfer Learning and Deep Learning.