Diabetes Mellitus, an increasingly prevalent endocrine disorder, presents a formidable challenge worldwide. Numerous nations diligently attempt to alleviate the impact of this ailment by employing various methodologies to predict its symptoms. Among the countless complications that affect individuals with diabetes, one particularly insidious indicator is diabetic retinopathy, a condition that detrimentally affects the eyes. This study aims to employ cutting-edge deep learning techniques, specifically convolutional neural networks (CNN), to construct a predictive model for diabetic retinopathy. Researchers have precisely analyzed and compared a plethora of scholarly articles pertaining to deep learning methodologies employed in the diagnosis of this visual affliction. These approaches have been accurately validated using data samples, with the model's efficacy being evaluated using accuracy metrics for datasets both with and without instances of diabetic retinopathy.

Artificial Intelligence, Machine Learning, Diabetes Retinopathy, Classification, Diabetes, Healthcare.