Automatic grading of non-proliferative diabetic retinopathy
Author:
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42600-023-00298-4.pdf
Reference48 articles.
1. AbdelMaksoud E, Barakat S, Elmogy M. A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique. Medical & Biological Engineering & Computing. 2022;60(7):2015–38.
2. Acharya UR, Lim CM, Ng EYK, Chee C, Tamura T. Computer-based detection of diabetes retinopathy stages using digital fundus images. Proceedings of the institution of mechanical engineers, part H: journal of engineering in medicine. 2009;223(5):545–53. https://doi.org/10.1243/09544119JEIM486.
3. Acharya UR, Ng EYK, Tan JH, Sree SV, Ng KH. An integrated index for the identification of diabetic retinopathy stages using texture parameters. Journal of medical systems. 2012;36:2011–20.
4. Agurto C, Murray V, Barriga E, Murillo S, Pattichis M, Davis H, Russell S, Abràmoff M, Soliz P. Multiscale AM-FM methods for diabetic retinopathy lesion detection. IEEE transactions on medical imaging. 2010;29(2):502–12. https://doi.org/10.1109/TMI.2009.2037146.
5. Andonová M, Pavlovičová J, Kajan S, Oravec M, Kurilová V (2017) Diabetic retinopathy screening based on CNN. In: 2017 International Symposium ELMAR, IEEE, pp 51–54
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