• Dr. Vijayalaxmi Mekali
• Nithya S N
• Nethra R
• Priya E
• Shanvi B P

Diabetic Retinopathy is a complication of diabetes that targets the eyes by damaging the retinal blood vessels. Initially, it is asymptomatic or causes fluctuating vision problems. As it becomes severe, it affects both eyes and eventually causes partial or complete vision loss. Primarily occurs when the blood sugar level is unmanageable. Therefore, a person with diabetes mellitus is always at a high risk of acquiring this disease. Early detection can deter the contingency of complete and permanent blindness. Thus, requires an efficient screening system. The present work considers a deep learning methodology specifically a residual network (ResNet-50), a deep Convolutional Neural Network, which is applied for the early detection of diabetic retinopathy. It classifies the fundus images based on their severity levels as No DR, Mild, Moderate, Severe and Proliferative DR. The datasets that are taken into consideration are Diabetic Retinopathy detection 2015 and Aptos 2019 blindness detection which are both obtained from Kaggle. The proposed method is accomplished through various steps: Data collection, Pre-processing, Augmentation, and Modelling. The main aim of this work is to develop a robust system for detecting DR automatically.

Diabetic Retinopathy(DR), Convolutional Neural Network(CNN), Deep Learning, ResNet-50, Fundus images, Classification, Pre-processing.

"Detection of Diabetic Retinopathy", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 2, page no.e826-e832, February-2023, Available :http://www.jetir.org/papers/JETIR2302497.pdf

"Detection of Diabetic Retinopathy", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 2, page no. ppe826-e832, February-2023, Available at : http://www.jetir.org/papers/JETIR2302497.pdf