Diabetic Retinopathy Detection from Fundus Images Using Machine Learning Techniques : A Review
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Computer Science Applications
Link
https://link.springer.com/content/pdf/10.1007/s11277-021-08817-1.pdf
Reference30 articles.
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2. Agurto, C., et al. (2014). A multiscale optimization approach to detect exudates in the macula. IEEE J. Biomed. Heal. Informatics, 18(4), 1328–1336. https://doi.org/10.1109/JBHI.2013.2296399
3. Akram, M. U., Tariq, A., Khan, S. A., & Javed, M. Y. (2014). Automated detection of exudates and macula for grading of diabetic macular edema. Computer Methods and Programs in Biomedicine, 114(2), 141–152. https://doi.org/10.1016/j.cmpb.2014.01.010
4. Pereira, C., Gonçalves, L., & Ferreira, M. (2015). Exudate segmentation in fundus images using an ant colony optimization approach. Inf. Sci. (Ny), 296(1), 14–24. https://doi.org/10.1016/j.ins.2014.10.059
5. M. Usman Akram, S. Khalid, A. Tariq, S. A. Khan, and F. Azam. “Detection and classification of retinal lesions for grading of diabetic retinopathy”. Comput. Biol. Med. 45 1 161–171, 2014. https://doi.org/10.1016/j.compbiomed.2013.11.014
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