Advancing Computer-Assisted Diabetic Retinopathy Grading: A Super Learner Ensemble Technique for Fundus Imagery

Abstract

Diabetic retinopathy (DR) is a severe complication of diabetes mellitus and is a predominant global cause of blindness. The accuracy of DR grading is of paramount importance to enable timely and appropriate clinical interventions. This study presents an innovative and comprehensive approach to DR grading that combines convolutional neural networks with an ensemble of diverse machine learning algorithms, referred to as a super learner ensemble. Our methodology includes a preprocessing pipeline designed to enhance the quality of the fundus images in the dataset. To further refine DR grading, we introduce a novel feature extraction model named “RetinaXtract” in conjunction with advanced machine learning classifiers. Statistical analysis tools, specifically the Friedman and Nemenyi tests, are employed to identify the most effective machine learning algorithms. Subsequently, a super learner ensemble is devised by integrating the predictions of the highest-performing machine learning algorithms. This ensemble approach captures a wide range of patterns, thereby enhancing the system's ability to accurately distinguish between different DR stages. Notably, accuracy rates of 99.64%, 99.51%, and 99.16% are achieved on the IDRiD, Kaggle, and Messidor datasets, respectively. This research represents a significant contribution to the field of DR grading, offering a balanced, efficient, and precise classification solution. The introduced methodology has demonstrated substantial promise and holds significant potential for practical applications in the detection and grading of DR from fundus images, ultimately leading to improved clinical outcomes in ophthalmology.