Title |
Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes
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Published in |
BMC Bioinformatics, April 2014
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DOI | 10.1186/1471-2105-15-106 |
Pubmed ID | |
Authors |
Gábor Márk Somfai, Erika Tátrai, Lenke Laurik, Boglárka Varga, Veronika Ölvedy, Hong Jiang, Jianhua Wang, William E Smiddy, Anikó Somogyi, Delia Cabrera DeBuc |
Abstract |
Artificial neural networks (ANNs) have been used to classify eye diseases, such as diabetic retinopathy (DR) and glaucoma. DR is the leading cause of blindness in working-age adults in the developed world. The implementation of DR diagnostic routines could be feasibly improved by the integration of structural and optical property test measurements of the retinal structure that provide important and complementary information for reaching a diagnosis. In this study, we evaluate the capability of several structural and optical features (thickness, total reflectance and fractal dimension) of various intraretinal layers extracted from optical coherence tomography images to train a Bayesian ANN to discriminate between healthy and diabetic eyes with and with no mild retinopathy. |
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