At present days, DR becomes a more common disease affecting the eyes because of drastic rise in the glucose level of blood. Almost half of the people under the age of 70's get severely affected due to diabetes. The earlier recognition and proper medication results to loss of vision in several DR patients. When the warning signs are identified, the severity level of the disease has to be validated to take decisions regarding the proper treatment. The current research focuses on the concept of classifying the images of DR fundus based on the severity level using a deep learning model. This paper proposes a deep learning based automated detection and classification model for fundus diabetic retinopathy (DR) images. The proposed method involves several processes namely preprocessing, segmentation and classification. Initially, preprocessing stage is carried out to get rid of the unnecessary noise exist in the edges. Next, histogram based segmentation takes place to extract the useful regions from the image. Then, synergic deep learning (SDL) model is applied to classify DR fundus images to various severity levels. The justification of the presented SDL model is carried out on Messidor DR dataset. The experimentation indicated that the presented SDL model offers better classification over the existing models.

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