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Diabetic retinopathy is a chronic progressive eye disease associated with a group of eye problems as a complication of diabetes. This disease may cause severe vision loss or even blindness. Specialists analyze fundus images in order to diagnose them and to give specific treatments. Fundus images are photographs taken of the retina using a retinal camera, this is a non-invasive medical procedure that provides a way to analyze the retina in patients with diabetes. The correct classification of these images depends on the ability and experience of specialists, and also the quality of the images. In this paper, we present a method for diabetic retinopathy detection using MATLAB. This method is divided into three stages: the first stage is pre-processing which involves disk segmentation and vessel segmentation. In the next stage which is feature extraction various features like variance, mean, skew, and standard deviation of texture are calculated using LBP and watershed algorithms. Finally, the classification is done using the SVM by creating a hyperplane and comparing the features with a dataset.