Privacy-preserving detection and classification of diabetic retinopathy using federated learning with FedDEO optimization

Abstract

Diabetic retinopathy (DR) is a major cause of blindness among adults worldwide. Detecting and classifying DR early is essential for timely treatment and prevention of vision loss. This study introduces a new approach to identify and classify DR by using federated learning (FL) environment and Federated differential evolution optimization (FedDEO). FL enables collaborative learning across multiple decentralized devices while maintaining data privacy. FedDEO optimization enhances the model's performance by fine-tuning hyperparameters in a distributed manner. The research achieves state-of-the-art accuracy in DR classification using the MESSIDOR dataset for training and testing. The findings suggest that integrating FedDEO optimization within the FL framework can significantly improve DR detection, offering a scalable and privacy-preserving solution for ophthalmic healthcare. The FedDEO algorithm optimizes hyperparameters, including learning rates and batch sizes, to enhance model performance. The experiments show that FedDEO works effectively for the MESSIDOR dataset to achieve a high classification accuracy, specificity, recall, and F1-Measure of 96.98%, 98.12%, 97.12%, and 98.00% while preserving privacy. The results demonstrate that implementing these algorithms in an FL environment significantly improves privacy and performance compared to other approaches. Specifically, the proposed technique outperforms others in terms of accuracy, specificity, recall, and F1 score when applied to categorizing retinal images.

Keywords

• Diabetic retinopathy
• fundus images
• deep learning
• federated learning
• machine learning
• federated differential evolution optimization