A Study Of Diabetics Retinopathy Using Fundus Camera
DOI:
https://doi.org/10.53555/jaz.v44iS8.3910Keywords:
Diabetic Retinopathy, Nonproliferative diabetic retinopathy, Proliferative diabetic retinopathyAbstract
Diabetic retinopathy can only be diagnosed with a full dilated eye exam. Drops in your eyes dilate (enlarge) your pupils so your doctor can see inside your eyes better during the test. In close quarters, the drops may cause blurry vision until they wear off, which might take several hours.Fundus photography can be used to track the progression of retinal disease over time, and it's becoming more used in diabetic retinopathy screenings. Patients with media opacity, such as vitreous haemorrhage or cataract, may benefit from B-scan ultrasonography.
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References
Prevalence of diabetic retinopathy in the United States, 2005–2008. Zhang X, Saaddine JB, Chou CF, et al. 304(6):649e656 in JAMA.
The Early Treatment Diabetic Retinopathy Study Research Group is a group of researchers who study the effects of early treatment on diabetic retinopathy. Using stereoscopic colour fundus pictures to grade diabetic retinopathy is an extension of the modified Airlie House classification. Ophthalmology. 1991;98(5 Suppl):786e806. ETDRS report number 10.
Mookiah MRK, Acharya UR, Chua CK, et al. Computer-aided diagnosis of diabetic retinopathy: a review. Comput Biol Med. 2013;43(12):2136e2155.
Winder RJ, Morrow PJ, McRitchie IN, et al. Algorithms for digital image processing in diabetic retinopathy. Comput Med Imag Graphics. 2009;33(8):608e622.
Image processing-based automatic diagnosis of glaucoma utilising wavelet features of segmented optic disc from fundus image, Singh A, Dutta MK, ParthaSarathi M, et al. 2016;124:108e120 in Comput Methods Prog Biomed.
Bock R, Meier J, Nyúl LG, et al. Glaucoma risk index: auto-mated glaucoma detection from color fundus images. Medical Image Analysis. 2010;14(3):471e481.
Issac, A., Partha Sarathi, M., and M.K. Dutta. An image processing technique based on adaptive thresholds for better glaucoma detection and classification. 229–244 in Comput Methods Prog Biomed, 2015.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436e444.
Soomro TA, Afifi AJ, Zheng L, Soomro S, Gao J, Hellwich O, et al. Deep Learning Models for Retinal Blood Vessels Segmentation: A Review. IEEE Access. 2019;7:71696–71717. [Google Scholar]
Sun Y, Zhang D. Diagnosis and Analysis of Diabetic Retinopathy Based on Electronic Health Records. IEEE Access. 2019;7:86115–86120. [Google Scholar]
L. Guariguata, D. R. Whiting, I. Hambleton, J. Beagley, U. Linnenkamp, and J. E. Shaw. Diabetes prevalence figures for 2013 and estimations for 2035. Diabetes Research and Clinical Practice, vol. 103, no. 2, pp. 137–149, 2014. [PubMed] [Source: Google Scholar]
El-Samie FEA, Shahin EM, Taha TE, Al-Nuaimy W, Rabaie S El, Zahran OF 2012 8th International Computer Engineering Conference (ICENCO); pp. 20–25; Automated diagnosis of diabetic retinopathy in hazy digital fundus images. [Source: Google Scholar]
Diagnosis of Diabetic Retinopathy Using Deep Neural Networks. Gao Z, Li J, Guo J, Chen Y, Yi Z, Zhong J. 3360–3370 in IEEE Access, 2019. [Source: Google Scholar]
Poostchi M, Silamut K, Maude RJ, Jaeger S, Thoma G. Image analysis and machine learning for detecting malaria. Translational Research. 2018;194:36–55. [PMC free article] [PubMed] [Google Scholar]
Automatic Diabetic Retinopathy Detection Using Digital Image Processing; 2018 International Conference on Communication and Signal Processing (ICCSP); pp. 72–76; Palavalasa KK, Sambaturu B. [According to Google Scholar]
Chandran A, Nisha KK, Vineetha S. Computer aided approach for proliferative diabetic retinopathy detection in color retinal images; 2016 International Conference on Next Generation Intelligent Systems (ICNGIS); 2016; pp. 1–6. [Google Scholar]
Mansour RF. Evolutionary Computing Enriched Computer-Aided Diagnosis System for Diabetic Retinopathy: A Survey. IEEE Reviews in Biomedical Engineering. 2017;10:334–349. [PubMed] [Google Scholar]
Agarwal S, Acharjya K, Sharma SK, Pandita S. Automatic computer aided diagnosis for early diabetic retinopathy detection and monitoring: A comprehensive review; 2016 Online International Conference on Green Engineering and Technologies (IC-GET); 2016; pp. 1–7. [Google Scholar]
Lin, D.Y., Blumenkranz, M.S., R.J. Brothers, and D.M. Grosvenor. For diabetic retinopathy screening, the sensitivity and specificity of single-field nonmydriatic monochromatic digital fundus photography with remote image interpretation were compared to ophthalmoscopy and standardised mydriatic colour photography. 134(2): 204e213. Am J Ophthalmol. 2002;134(2): 204e213.
4. M. Detry-Morel, T. Zeyen, P. Kestelyn, and others The non-mydriatic fundus camera and the frequency doubling perimeter were used to screen for glaucoma in a general population. Eur J Ophthalmol 14(5):387-393, 2004.
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