• Suma B
• Sandeep Kumar

The most common disorder affecting millions of people vision around the world is Diabetic Retinopathy(DR). As the population of diabetic patients is enormous early detection and diagnosis is necessary to maintain happy life. Conventional diabetes detection method is tedious. The classification done by giving direct features are exploited. Using deep neural networks either only binary classification is done or multi classification is done on very small dataset which can not be used for deployment of DR classification application. This project proposes an approach to create a novel dataset which can be used to perform image classification using CNN model and diagnose retina images perfectly. Which will help millions of diabetic patients to diagnose in very less time and accurately and ophthalmologists can use this in there diagnosis.

Convolution neural network, Diabetes, Diabetic retinopathy, Microaneurysms

"DIABETIC RETINOPATHY DETECTION USING DEEP LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.j14-j22, June-2022, Available :http://www.jetir.org/papers/JETIR2206903.pdf

"DIABETIC RETINOPATHY DETECTION USING DEEP LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppj14-j22, June-2022, Available at : http://www.jetir.org/papers/JETIR2206903.pdf