Towards Convolutional Neural Networks for Diabetic-Retinopathy Detection - Oliveira, Vitor A.; Santos, Helida; Lucca, Giancarlo; Oliveira, Lizandro; Yamin, Adenauer; Reiser, Renata (Anais do Workshop-Escola de Informática Teórica (WEIT); 2025: Anais do VIII Workshop-Escola de Informática TeóricaDO - 10.5753/weit.2025.40448, 2025)
This study explores the potential of two lightweight convolutional neural networks, EfficientNet-B3 and DenseNet-169, for the diagnosis of Diabetic Retinopathy (DR), a microvascular complication of chronic hyperglycemia and a leading cause of preventable blindness among working-age adults. Threatening vision in nearly a million people who live with diabetes, timely and accurate detection is crucial. We analyze related theoretical concepts and metric evaluations, demonstrating the feasibility of compact CNN backbones for real-world deployment. All results are derived from the public EyePACS dataset, with the quadratic-weighted kappa metric used for evaluation.