ABSTRACT
Diabetic Retinopathy (DR) is a chronic health disease which requires early detection and treatment. It is important to identify DR using an intelligent system for faster prediction since manual examination and detection of the disease are unreliable and highly prone to error. Therefore, various researchers and medical experts have adopted and approached for advanced feature extraction and image classification, for early DR detection. Diabetic Retinopathy is a consequence of diabetes that affects the eyes. Damaged blood vessels in the retina, a light-sensitive tissue, are the primary cause of DR. If the patient has a long-term case of diabetes and the blood sugar level is not regulated consistently, the odds of this issue developing in the eye increase. Diabetic Retinopathy is one of the most common causes of blindness in the Western countries. Preventing Diabetic Retinopathy has found to be quite beneficial when people with diabetes are monitored regularly. This process is shown to be essential if Diabetic Retinopathy is discovered in its early stages due to the availability of treatment. Diabetic Retinopathy, the main cause of blindness among working-age adults, affects millions of individuals. Diabetic Retinopathy is a medical disorder in which diabetes mellitus causes damage to the retina. Diabetic Retinopathy is diagnosed using colored fundus images, which requires trained clinicians to recognize the presence and importance of several tiny characteristics, making it a time-consuming task. We present a convolutional neural network CNN-based technique to detect diabetic retinopathy in fundus images in this project. This project is developed in matlab.
PROJECT OUTPUT
PROJECT DEMO VIDEO
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