Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes
暂无分享,去创建一个
Hong Jiang | Erika Tátrai | Anikó Somogyi | Boglárka Varga | Gábor Somfai | Lenke Laurik | Veronika Ölvedy | Jianhua Wang | William E Smiddy | Delia DeBuc
[1] M F Marmor,et al. The interphotoreceptor matrix mediates primate retinal adhesion. , 1995, Archives of ophthalmology.
[2] Delia Cabrera DeBuc,et al. Reliability and reproducibility of macular segmentation using a custom-built optical coherence tomography retinal image analysis software. , 2009, Journal of biomedical optics.
[3] Mark A. Musen,et al. Medical applications of artificial neural networks : connectionist models of survival , 1996 .
[4] K Nakamura,et al. Effect of an artificial neural network on radiologists' performance in the differential diagnosis of interstitial lung disease using chest radiographs. , 1999, AJR. American journal of roentgenology.
[5] Wei Gao,et al. Improving image segmentation performance and quantitative analysis via a computer-aided grading methodology for optical coherence tomography retinal image analysis. , 2010, Journal of biomedical optics.
[6] U. Rajendra Acharya,et al. Identification of different stages of diabetic retinopathy using retinal optical images , 2008, Inf. Sci..
[7] Carmen A Puliafito,et al. Automated detection of retinal layer structures on optical coherence tomography images. , 2005, Optics express.
[8] J. Shaw,et al. Global estimates of the prevalence of diabetes for 2010 and 2030. , 2010, Diabetes research and clinical practice.
[9] Wei Gao,et al. Assessing the Performance of Optical Properties Determination of Intraretinal Layers in Healthy Normal and Type 1 Diabetic Eyes using Optical Coherence Tomography , 2011 .
[10] D L DeMets,et al. The Wisconsin epidemiologic study of diabetic retinopathy. III. Prevalence and risk of diabetic retinopathy when age at diagnosis is 30 or more years. , 1984, Archives of ophthalmology.
[11] Yuichi Nishiyama,et al. Development of novel mucoadhesive pellets of metformin hydrochloride , 2009, Archives of pharmacal research.
[12] T. Williamson,et al. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. , 1996, The British journal of ophthalmology.
[13] James G. Fujimoto,et al. Stratus OCT Image Quality Assessment , 2004 .
[14] Chanjira Sinthanayothin,et al. Automated screening system for diabetic retinopathy , 2003, 3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the.
[15] Sobha Sivaprasad,et al. Retinal Neuronal Changes In People With Diabetes , 2012 .
[16] Richard S. Sutton,et al. Neural networks for control , 1990 .
[17] D L DeMets,et al. The Wisconsin Epidemiologic Study of Diabetic Retinopathy. VI. Retinal photocoagulation. , 1987, Ophthalmology.
[18] J W Moul,et al. Applications of neural networks in urologic oncology. , 1998, Seminars in urologic oncology.
[19] Haogang Zhu,et al. Quantifying discordance between structure and function measurements in the clinical assessment of glaucoma. , 2011, Archives of ophthalmology.
[20] Yoshinori Mitamura,et al. Optical coherence tomography for complete management of patients with diabetic retinopathy. , 2010, Current diabetes reviews.
[21] BMC Bioinformatics , 2005 .
[22] Haogang Zhu,et al. Predicting visual function from the measurements of retinal nerve fiber layer structure. , 2010, Investigative ophthalmology & visual science.
[23] Te-Won Lee,et al. Bayesian machine learning classifiers for combining structural and functional measurements to classify healthy and glaucomatous eyes. , 2008, Investigative ophthalmology & visual science.
[24] Maciej Wojtkowski,et al. Retinal assessment using optical coherence tomography , 2006, Progress in Retinal and Eye Research.
[25] Davide Dazzi,et al. Classification and prediction of the progression of thyroid-associated ophthalmopathy by an artificial neural network. , 2002, Ophthalmology.
[26] Gábor Márk Somfai,et al. Early detection of retinal thickness changes in diabetes using Optical Coherence Tomography. , 2010, Medical science monitor : international medical journal of experimental and clinical research.
[27] A E Leure-duPree,et al. Electron-opaque inclusions in the rat retinal pigment epithelium after treatment with chelators of zinc. , 1981, Investigative ophthalmology & visual science.
[28] Ricardo S Silva. Source , 2000, BMJ : British Medical Journal.
[29] S. Agatonovic-Kustrin,et al. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. , 2000, Journal of pharmaceutical and biomedical analysis.
[30] Sobha Sivaprasad,et al. Prevalence of diabetic retinopathy in various ethnic groups: a worldwide perspective. , 2012, Survey of ophthalmology.
[31] P. Lapuerta,et al. Neural Network Assessment of Perioperative Cardiac Risk in Vascular Surgery Patients , 1998, Medical decision making : an international journal of the Society for Medical Decision Making.
[32] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[33] R. Raman,et al. Is neuronal dysfunction an early sign of diabetic retinopathy? Microperimetry and Spectral Domain Optical Coherence Tomography (SD-OCT) Study in individuals with diabetes, but no diabetic retinopathy , 2009, Eye.