A Cascaded Edge-Cloud Architecture for Automated Diabetic Retinopathy Screening

תקציר מקורי באנגליתarXiv:2605.14108v2 Announce Type: replace-cross Abstract: 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% s