Authors - Himangshu Sarma, Madhumita Banerjee, Chandrajit Choudhury Abstract - Diabetic Retinopathy starts at a light level with no visible symptoms, but it can lead to severe pain and blindness as the disease progresses. Clinically, DR is diagnosed by looking for retinal detachment or utilizing imaging techniques like fundus imaging or optical tomography. The Early Diabetic Retinopathy Study is one of the established DR staging schemes. Image Processing has played a significant role in improving the methods used to detect the disease automatically. It has a huge role in assisting ophthalmologists in the screening process, as manually screening by ophthalmologists consumes more time, sometimes there may also be error. Although there are a number of algorithms used in detection, therefore, in this work we have studied and implemented various DR detection methods. To differentiate among various classes, we also prepared KAGGLE APTOS dataset containing the GLCM, LTP, GLRLM, LMeP, CLBP, CSLBP and LBP features extracted from the image dataset. And the classification is performed in the dataset, whilst achieving an accuracy of 97% on testing dataset and 96% for training dataset using SVM multi classifier.
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