Deep Learning Approaches in Anticipating Diabetic Retinopathy Progression

One of the most prevalent complications caused by diabetes that still causes loss of vision and blindness worldwide is diabetic retinopathy. For treatments to be effective, early and correct identification is essential. Despite advancements in medicine, a complex automated solution is still required to address the impending threat of blindness due to the difficult early diagnosis of diabetes-related retinopathy, a key contributor to visual impairment. Using a well-selected diabetic retinopathy dataset, our novel approach applies transfer learning techniques using EfficientNetB3, ResNet50, and DenseNet169. Our model is strengthened by a new hybrid oversampling method that addresses class imbalance, and its training incorporates cutting-edge components like differential regularization, adaptive dropout, and Adamax optimization for increased accuracy.Test results demonstrate the remarkable sensitivity, specificity, accuracy, and F1 score of our model at various degrees of retinopathy severity, establishing a new standard for diagnostic dependability in the identification of diabetic retinopathy. By utilizing cutting-edge deep learning architectures, our method establishes a strong basis upon which future developments will be built. These developments will include adaptive optimization strategies, ensemble learning to improve resilience, and self-supervised pre-training, all of which will accelerate the ongoing advancement of diabetic retinopathy detection accuracy.