DRG-Net: Diabetic Retinopathy Grading Network using Graph Learning with Extreme Gradient Boosting Classifier

Abstract

Diabetic retinopathy (DR) is a leading cause of blindness that occurs in different age groups. So, the early detection of DR can save millions of people from blindness issues. Further, the manual analysis of DR requires much processing time and experienced doctors. Hence, computer-aided diagnosis (CAD)-based artificial intelligence models have been developed for an early DR prediction. However, the state-of-the-art methodologies are failed to extract the deep balanced features, which resulted in poor classification performance. Therefore, this work implements the DR grading network (DRG-Net) using graph learning properties. Initially, synthetic minority over-sampling technique (SMOTE) is applied on EyePACS and Messidor dataset to balance the instances of each DR class into uniform level. Then, a deep graph correlation network (DGCN) is applied to extract the class-specific features by identifying the relationship. Finally, an extreme gradient boosting (XGBoost) classifier is employed to perform the DR classification with the pre-trained balanced features obtained using SMOTE-DGCN. The obtained simulation results performed on the EyePACS dataset and the Messidor dataset disclose that the proposed DRG-Net resulted in higher performance than state-of-the-art DR grading classification approaches, with accuracy, sensitivity, and specificity of 99.01%, 99.01%, and 98.43% for the EyePACS dataset, respectively, and 99.6%, 99.08%, and 100% for the Messidor dataset.