RetinaNeXt: ConvNeXt-Driven Deep Learning for Diabetic Retinopathy Severity Grading

This study introduces RetinaNeXt, a deep learning model based on the ConvNeXt-Base architecture for grading the severity of diabetic retinopathy (DR) into five classes. The goal is to achieve robust, interpretable, and cross-dataset generalization using a scalable convolutional backbone. RetinaNeXt integrates transfer learning from pre-trained ConvNeXt, class balancing via Focal Loss, and extensive data augmentation. It is trained on the EyePACS dataset and externally validated on APTOS and IDRiD datasets. Grad-CAM visualizations are used to interpret the model’s focus on pathological regions. RetinaNeXt achieved 95.26% accuracy on EyePACS, 94.85% on APTOS, and 90.29% on IDRiD, demonstrating strong cross-domain generalization. Class-wise analysis revealed consistent performance across all ICDR grades, with improved sensitivity in minority classes, attributed to Focal Loss. The proposed RetinaNeXt framework combines architectural efficiency, interpretability, and generalization, making it suitable for real-world DR screening applications. Future work will explore lightweight deployment and integration with federated learning for privacy-preserving detection of DR.