Automated analysis of fundus images for the diagnosis of retinal diseases: a review
Author:
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42600-023-00320-9.pdf
Reference192 articles.
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2. Abràmoff MD et al. Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmology 2013; 131(3): 351–57. https://doi.org/10.1001/jamaophthalmol.2013.1743 (February 16, 2022).
3. Abràmoff MD et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investigative Ophthalmology & Visual Science 2016;57(13): 5200–5206. https://doi.org/10.1167/iovs.16-19964 (February 16, 2022).
4. Agurto C et al. Automatic detection of diabetic retinopathy and age-related macular degeneration in digital fundus images. Investigative Ophthalmology & Visual Science 2011;52(8): 5862–71. https://doi.org/10.1167/iovs.10-7075 (April 30, 2021).
5. Ahmad Hijazi MH, Coenen F, Zheng Y. Retinal image classification using a histogram based approach. In The 2010 International Joint Conference on Neural Networks (IJCNN), 2010;1–7.
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