Analysis of Approaches for Automated Glaucoma Detection and Prediction System-Reference-Cited by-同舟云学术

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Analysis of Approaches for Automated Glaucoma Detection and Prediction System

Published:2021 Issue: Volume: Page:329-343
ISSN:1860-949X
Container-title:Studies in Computational Intelligence
language:
Short-container-title:

Author:

Mishra Upasana,Raikwal Jagdish

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-68291-0_26

Reference28 articles.

1. Khalil, T., Khalid, S., Syed, A.M.: Review of Machine Learning Techniques for Glaucoma Detection and Prediction. Science and Information Conference 2014, London, UK, 438 DOI:https://doi.org/10.1109/sai.2014.6918224

2. Daniele, M.S., Barros, J.C.C., Moura, Cefas R. Freire, Alexandre C. Taleb, Ricardo A. M. Valentim, Philippi S. G. Morais: Machine learning applied to retinal image processing for glaucoma detection: Review and perspective. BioMed Eng OnLine, 2020. DOI:https://doi.org/10.1186/s12938-020-00767-2

3. Soorya, M., Ashish, I., Malay, K.D.: An automated and robust image processing algorithm for glaucoma diagnosis from fundus images using novel blood vessel tracking and bend point detection. Int. J. Med. Inform., 52–70 (2018). DOI: https://doi.org/10.1016/j.ijmedinf.2017.11.015

4. Lee, J. et al.: Machine learning classifiers-based prediction of normal-tension glaucoma progression in young myopic patients. Jpn. J. Ophthalmol. @Springer 64(1), 68–76 (2020). https://doi.org/10.1007/s10384-019-00706-2

5. Niwas, S.I, Lin, W, Bai, X, Kwoh, C.K, Sng, C.C, Aquino, M.C, Chew, P.T.: Reliable feature selection for automated angle closure glaucoma mechanism detection. J. Med. Syst. 39(3), 21 (2015). https://doi.org/10.1007/s10916-015-0199-1

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1. Glaucoma Detection Approach based on Colour Fundus Pictures;2024 11th International Conference on Computing for Sustainable Global Development (INDIACom);2024-02-28

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