| Summary: | ) Diabetic retinopathy (DR is one of the complications on retina caused by
diabetes. The study aims to develop a system that can be used for automatic mass
screenings of diabetic retinopathy. Four classes are identified: normal retina,
non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy
(PDR), and macular edema (ME). Ninety-seven retinal fundus images were used
in this study. Six different texture features such as maximum probability,
correlation, contrast, energy, homogeneity, and entropy were extracted from the
digital fundus images using gray level cooccurence matrix (GLCM). The features
were fed into a backpropagation neural network classifier for automatic
classification. The proposed approach is able to classify with sensitivity 100%,
specificity 100%, and accuracy 90.68%
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