Application of deep learning approaches for classification of diabetic retinopathy stages from fundus retinal images: a survey
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17254-0.pdf
Reference152 articles.
1. Wang RY, Zhang RP, Chen J, Sun R, Yang XY, Ke H, Chen DH (2013) Cai, Prevalence and risk factors for diabetic retinopathy in a high-risk Chinese population. BMC Public Health 13:633. https://doi.org/10.1186/1471-2458-13-633
2. Sussman EJ, Tsiaras WG, Soper KA (1982) Diagnosis of diabetic eye disease. JAMA Ophthalmol 247(23):3231–3234
3. Whiting DR, Guariguata L, Weil C, Shaw J (2011) IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract 94(3):311–321. https://doi.org/10.1016/j.diabres.2011.10.029
4. Keenan TD, Johnston RL, Donachie PH, Sparrow JM, Stratton IM, Scanlon P (2013) United Kingdom National Ophthalmology Database Study: Diabetic Retinopathy; Report 1: prevalence of centre-involving diabetic macular edema and other grades of maculopathy and retinopathy in hospital eye services. Eye (London) 27:1397–1404
5. Klein R, Klein BE, Moss SE, Davis MD, DeMets DL (1984) The wisconsin epidemiologic study of diabetic retinopathy II. Prevalence and risk of diabetic retinopathy when age at diagnosis is less than 30 years. Arch Ophthalmol 102:520–526
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A Deep Learning Model for Detecting Diabetic Retinopathy Stages with Discrete Wavelet Transform;Applied Sciences;2024-05-23
2. A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images;PeerJ Computer Science;2024-04-29
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