Glaucoma detectıon and classıfıcatıon by usıng hysteresıs thresholdıng based IAOAWTO and ICNNBTL classıfıer
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17148-1.pdf
Reference50 articles.
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2. Markus A Mayer, Joachim Hornegger, Christian Y Mardin, Ralf P Tornow, (2010) ‘Retinal nerve layer segmentation on FD-OCT scans of normal subjects and glaucoma patients’, Biomed Opt Express, vol. 1, no. 5. https://doi.org/10.1364/BOE.1.001358
3. Sau PC, Gupta M, Kumar D (2021) A Comparative Study: Glaucoma Detection Using Deep Neural Networks, In Proceedings of International Conference on Big Data, Machine Learning and their Applications, pp. 85-97. Springer, Singapore. https://doi.org/10.1007/978-981-15-8377-38
4. Ali MAS, Balasubramanian K, Krishnamoorthy GD, Muthusamy S, Pandiyan S, Panchal H, Mann S, Thangaraj K, El-Attar NE, Abualigah L et al (2022) Classification of Glaucoma Based on Elephant-Herding Optimization Algorithm and Deep Belief Network. Electronics 11:1763. https://doi.org/10.3390/electronics11111763
5. Xu, Y, Duan, L, Lin, S, Chen, X, Wong, DWK, Wong, TY, Liu, J (2014) ‘Optic cup segmentation for glaucoma detection using low-rank super pixel representation’, proceeding of International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 788–795. https://doi.org/10.1007/978-3-319-10404-1_98
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