Diabetes is a major cause of blindness. Patients with history of diabetes are more prone to diabetic retinopathy (DR), which is the damage of the retina due to diabetes. Computer-aided diagnosis (CAD) systems have been used to detect DR early, which can reduce the occurrence of blindness. In this paper we analyze the performance of robust texture analysis methods in order to improve the performance of DR CAD systems. We used six texture analysis methods: the color autocorrelogram, local binary pattern, histogram of oriented gradients, local directional number, co-occurrence matrix features and Gabor filters. We also propose new combinations of different texture analysis methods to further improve the detection results. The proposed CAD system gave promising DR detection results and it is on par with related methods.
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