• Madhuri Draksharam
• Prof. K.VENKATA RAO

Navigating the intricate landscape of diabetic retinopathy (DR) detection, this paper conducts a comprehensive analysis of contemporary research's deep learning methodologies. DR, a prevalent cause of vision impairment in individuals with diabetes, demands precise and timely diagnosis. The advent of deep learning has ushered in a new era, equipping researchers with sophisticated tools for image analysis. Structured to address the urgency of DR detection, this study explores the diverse comparison types prevalent in the field. From binary classification to multi-class categorization and lesion detection, deep learning methodologies are meticulously dissected. Visual aids, including bar charts and pie charts, enhance the accessibility of complex information, providing a nuanced understanding of prevailing trends. This research transcends individual studies, contributing to the broader dialogue within the scientific community. By amalgamating insights, it not only consolidates the current understanding of DR research but also lays the groundwork for future innovations. The synthesis serves as a compass for researchers and practitioners, guiding efforts towards transformative advancements in the critical realm of diabetic retinopathy analysis.

Diabetic retinopathy (DR), Deep learning, CNN, Binary classification.

"Advancements and Trends in Diabetic Retinopathy Analysis through Contemporary Deep Learning Methodologies", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.1-8, October-2024, Available :http://www.jetir.org/papers/JETIRGN06001.pdf

"Advancements and Trends in Diabetic Retinopathy Analysis through Contemporary Deep Learning Methodologies", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. pp1-8, October-2024, Available at : http://www.jetir.org/papers/JETIRGN06001.pdf