Deep learning-based algorithm for automated detection of glaucoma on eye fundus images
Author:
Funder
Uppsala Universitet
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11042-024-19989-w.pdf
Reference34 articles.
1. Liu H et al (2019) Development and validation of a deep learning system to detect glaucomatous optic neuropathy using fundus photographs. JAMA Ophthalmol 137(12):1353–1360. https://doi.org/10.1001/jamaophthalmol.2019.3501
2. Lalonde M, Beaulieu M, Gagnon L (2001) Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching. IEEE Trans Med Imaging 20(11):1193–1200. https://doi.org/10.1109/42.963823
3. Nawaz M, Nazir T, Javed A, Tariq U, Yong H-S, Khan MA, Cha JA (2022) Efficient deep learning approach to automatic Glaucoma detection using optic disc and optic cup localization. Sensors 22:434. https://doi.org/10.3390/s22020434
4. Walter, T., & Klein, J. C. (2001). Segmentation of color fundus images of the human retina: Detection of the optic disc and the vascular tree using morphological techniques. In Proceedings of the Second International Symposium on medical data analysis, 282–287. Springer, London, UK, UK, ISMDA ‘01. https://dl.acm.org/citation.cfm?id=646351.691036
5. Mohamed, N., Zulkifley, M., & Hussain, A. (2015). On analyzing various density functions of local binary patterns for optic disc segmentation. In 2015 IEEE Symposium on Computer Applications Industrial Electronics (ISCAIE), 37–41. DOI: https://doi.org/10.1109/ISCAIE.2015.7298324
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