Early Detection of Diabetic Retinopathy Using Enhanced Transfer Learning Techniques

Diabetic Retinopathy (DR) is a critical complication associated with diabetes, often leading to vision loss and blindness if not identified and managed promptly. This project focuses on the early detection and classification of various DR stages, a vital step toward better patient outcomes. We utilize transfer learning with EfficientNetB3, a model recognized for its efficient performance in image classification tasks. Through fine-tuning on a dataset of retinal images and using ImageNet pre-trained weights, the model achieves a higher accuracy in categorizing images into five classes: Healthy, Mild DR, Moderate DR, Proliferative DR, and Severe DR.To further enhance model robustness, various data augmentation techniques—including rotations, zooms, and horizontal flips—are applied to improve diversity within the training data. The dataset is carefully partitioned into training, validation, and testing subsets, allowing for precise monitoring of the model’s generalization capabilities and accuracy across different DR stages. Comprehensive performance evaluation is conducted using metrics such as accuracy, precision, recall, and a confusion matrix to analyze the model’s ability to accurately classify each DR stage. This robust approach shows great potential for advancing effective DR classification, ensuring timely and precise treatment strategies for patients at risk.