• Ananya B
• Prajna H S
• Shifa Sonal
• Kavya B
• Mrs. Rekha B N

Diabetic retinopathy (DR) remains a significant public health challenge, contributing to visual impairment and blindness among individuals with diabetes worldwide. With the exponential growth of medical imaging data, there has been a surge in research focused on leveraging deep learning techniques for the automated detection and classification of DR. This paper provides a comprehensive review of recent advancements in deep learning approaches for DR detection, highlighting the evolution of convolutional neural networks (CNNs) and their application in analyzing retinal fundus images. We discuss key methodologies, including network architectures, feature extraction, and classification strategies, as well as emerging trends such as transfer learning and ensemble methods.

Diabetic Retinopathy, Deep Learning, Neural Networks, Machine Learning, Image Analysis, Medical Imaging, Fundus Photography, Retinal Images, Feature Extraction, Classification Models, Convolutional Neural Networks (CNNs), Data Augmentation, Retinal Vessels, Hyperglycemia.

"Diabetic Retinopathy Evaluation using Deep Neural Networks ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.f547-f552, May-2024, Available :http://www.jetir.org/papers/JETIR2405563.pdf

"Diabetic Retinopathy Evaluation using Deep Neural Networks ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppf547-f552, May-2024, Available at : http://www.jetir.org/papers/JETIR2405563.pdf