VEnDR-Net: Voting Ensemble Classifier for Automated Diabetic Retinopathy Detection

Abstract

Diabetic Retinopathy (DR) is a significant eye disease, which is caused by the damage of retina. To provide the best timely treatment, it is necessary to detect DR in early stages. Firstly, advanced sequential image preprocessing and segmentation techniques are employed for accurate localizing and isolating the affected regions in retinal images. Secondly, a voting ensemble classifier is introduced using a deep neural network model, which combines the predictions of multiple CNN models i.e., ResNet50, VGG16, VGG19 and GoogLeNet to enhance the overall classification performance of the proposed model. Our proposed model, named VEnDR-Net (Voting Ensemble for Diabetic Retinopathy classification using deep neural networks), implements on the EyePACS dataset and achieves 0.97 sensitivity, 0.97 specificity, 0.98 accuracy, 0.98 precision, and 0.97 F1- Score, respectively. The enhancement of the performance is 1.49% in accuracy over the other existing models. Lastly, the research addresses the grading of diabetic retinopathy by aligning the classification results with a standardized grading system, providing clinicians with accurate severity assessment for effective treatment decision.