Abstract
Diabetic retinopathy is a complication in the eye due to the presence of exudates in the retinal blood vessels. Vision loss occurs by the gradual progression of these exudates. Hence identification of exudates is an essential step for screening of diabetic retinopathy. In the proposed method, the exudates are segmented by eliminating the blood vessels and optic disc from the fundus images. The Grey Level Co-occurrence Matrix (GLCM) features are extracted from the segmented image. These features are used for training and testing the three different classifiers such as Support Vector Machine (SVM), Scaled Conjugate Gradient Back Propagation Network (SCG-BPN) and Generalized Regression Neural Network (GRN). From the experimental results, the SVM classifier gives better accuracy compared to the other two classifiers. The images are taken from publicly available database Diabetic retinopathy image database 1 (Diaretdb1).
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Vanithamani, R., Renee Christina, R. (2018). Exudates in Detection and Classification of Diabetic Retinopathy. In: Abraham, A., Cherukuri, A., Madureira, A., Muda, A. (eds) Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016). SoCPaR 2016. Advances in Intelligent Systems and Computing, vol 614. Springer, Cham. https://doi.org/10.1007/978-3-319-60618-7_25
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DOI: https://doi.org/10.1007/978-3-319-60618-7_25
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