Federated Learning for Diabetic Retinopathy Diagnosis: Enhancing Accuracy and Generalizability in Under-Resourced Regions
This addresses the lack of ophthalmologists and diagnostic tools in under-resourced areas, though it is incremental as it applies an existing federated learning approach to a specific medical domain.
The paper tackled the problem of limited generalizability in diabetic retinopathy diagnosis for under-resourced regions by developing a federated learning system, achieving 93.21% accuracy on an unseen dataset and 91.05% on lower-quality images.
Diabetic retinopathy is the leading cause of vision loss in working-age adults worldwide, yet under-resourced regions lack ophthalmologists. Current state-of-the-art deep learning systems struggle at these institutions due to limited generalizability. This paper explores a novel federated learning system for diabetic retinopathy diagnosis with the EfficientNetB0 architecture to leverage fundus data from multiple institutions to improve diagnostic generalizability at under-resourced hospitals while preserving patient-privacy. The federated model achieved 93.21% accuracy in five-category classification on an unseen dataset and 91.05% on lower-quality images from a simulated under-resourced institution. The model was deployed onto two apps for quick and accurate diagnosis.