An Ensemble Performance Comparison of Diabetic Retinopathy Detection Algorithm in Retinal Fundus Images using Different Datasets

Diabetic Retinopathy (DR) is a prevalent eye condition among diabetic patients that can lead to irreversible vision loss if not detected and treated early. Early detection and continuous monitoring are crucial for effective management of DR. Traditional diagnostic methods, including visual acuity tests and non-invasive imaging, are often time-consuming and less efficient. Previous approaches, such as Residual Contrast Limited Adaptive Histogram Equalization (RCLAHE), have shown limitations in detecting DR. This research presents an Optimized Back Propagation based Deep Residual Learning Network (Op-BPDRLN) algorithm to enhance the detection of DR. By comparing the performance of different ensemble models on various retinal image datasets, this study aims to develop a more efficient and accurate algorithm for DR diagnosis. The comparison is based on key metrics such as accuracy, error, precision, and recall to determine the most effective classification model.