Deep Learning Models for Diabetic Retinopathy Detection: A Comparative Study

Diabetic Retinopathy (DR) is a leading cause of vision impairment globally, making accurate and efficient detection of DR stages from ophthalmological images critical for early diagnosis and treatment. In this study, we develop and evaluate the efficacy of four state-of-the-art deep learning models for classifying DR stages: a custom Convolutional Neural Network (CNN), VGG19, ResNet50, and Vision Transformer (ViT). These models are systematically applied to the Brazilian Multi-label Ophthalmological Dataset (BRSET), which consists of 16,266 retinal fundus images categorized into different DR severity levels. Each model’s performance is assessed based on metrics such as accuracy, F1-score, and AUC-ROC. Our results provide a comprehensive comparison of these architectures, highlighting their strengths and limitations in DR stage estimation, and offer insights into the suitability of these models for large-scale DR screening.