• S Oyyathevan
• Saladi D S N Vishnu Priya
• Goraparthi Rohan Durga Prasad
• Maradana Hemanth
• Nimmala Sai Krishna Karthik

Diabetic Retinopathy (DR) is an eye disease that damages the blood vessels in the retina, leading to vision impairment and potentially blindness if not detected in its early stages. DR is classified into five stages: normal, mild, moderate, severe, and Proliferative Diabetic Retinopathy (PDR). Traditionally, skilled ophthalmologists manually examine colored fundus images to diagnose DR, but this process is time-consuming and prone to human error. To address these limitations, various image processing techniques have been introduced to automatically detect DR and its progression from retinal images. This paper reviews and analyzes state-of-the-art deep learning methods and image processing approach. Utilization of Deep learning algorithms has aided the early diagnosis of Diabetic Retinopathy (DR).

Diabetic Retinopathy, Image Processing, Deep Learning, Convolutional Neural Networks, Classification.

"A Unified Deep Learning and Image Processing approach for Diabetic Retinopathy detection.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.d699-d707, December-2024, Available :http://www.jetir.org/papers/JETIR2412385.pdf

"A Unified Deep Learning and Image Processing approach for Diabetic Retinopathy detection.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppd699-d707, December-2024, Available at : http://www.jetir.org/papers/JETIR2412385.pdf