Detection of Diabetic Retinopathy Using a Transfer Learning Approach with DarkNet19

Abstract

The early detection of diabetic retinopathy (DR) in fundus images is crucial for preventing vision loss in diabetic patients. Deep learning models have played a significant role in advancing DR detection. This paper explores the relatively unexplored DarkNet19 model’s performance in comparison to well-established models like ResNet18 and ResNet50 for DR detection. A balanced dataset of healthy and DR images was created through standardization and augmentation techniques. The models underwent binary classification training and testing, and their performance was evaluated using accuracy and precision metrics. DarkNet19 outperformed the other models, achieving higher accuracy (0.7347) and precision (0.9233), demonstrating its potential to enhance early DR diagnosis and reduce the risk of vision loss. This research contributes to the field of DR detection, highlighting the effectiveness of DarkNet19.