VR-FuseNet: A Fusion of Heterogeneous Fundus Data and Explainable Deep Network for Diabetic Retinopathy Classification

Key Points

• Improved diabetic retinopathy classification is achieved with explainable deep networks and fundus data fusion.
• The metrics indicate a notable increase in classification accuracy rates due to the innovative model approach.
• Analysis incorporates heterogeneous fundus data using advanced algorithms to enhance detection precision.
• The findings highlight potential advancements for timely diabetic retinopathy detection in clinical settings.