A Cascaded Edge-Cloud Architecture for Automated Diabetic Retinopathy Screening

Diabetic Retinopathy (DR) is one of the leading causes of preventable blindness, and automated screening can help extend specialist capacity in resource-constrained clinical workflows. Cloud-based deep learning systems can provide strong grading performance, but they require image upload and reliable connectivity. We evaluate a two-tier edge-cloud cascade on the public APTOS 2019 Blindness Detection dataset. Tier 1 runs a lightweight MobileNetV3-small model locally to triage Referable DR (Classes 2-4) versus Non-referable DR (Classes 0-1). Tier 2 runs a RETFound-DINOv2 model in the cloud for ordinal severity grading only on images flagged as referable by Tier 1. On a stratified APTOS test split of 733 images, Tier 1 reaches 98.99% sensitivity and 84.37% specificity at a validation-tuned high-sensitivity threshold. The deployment-oriented safety-floor cascade forwards 49.52% of test images to Tier 2, reducing image-count cloud calls by 50.48% relative to a cloud-only model. In the deployed 4-class output space (Class 0-1 / Class 2 / Class 3 / Class 4), the safety-floor cascade obtains 77.49% accuracy and 0.7938 quadratic weighted kappa, while the cloud-only baseline obtains 80.76% accuracy and 0.8184 kappa. A Tier-2-override comparator improves accuracy to 80.49% and kappa to 0.8167, but downgrades 12 true referable cases after Tier 1 correctly forwarded them. We also report repeated stratified bootstrap variability, a prevalence-dependent cloud-call curve, raw-byte upload estimates, and CPU latency for the local tier. Index Terms: Diabetic retinopathy screening, retinal image analysis, edge-cloud computing, cascaded inference, teleophthalmology, clinical decision support, medical artificial intelligence, resource-constrained healthcare