Scalable and Efficient Deep Learning for Diabetic Retinopathy Classification on ARM-Based Architectures - Araújo, Thiago da Silva; Navaux, Philippe O. A. (Anais Estendidos do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais Estendidos do XXVI Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas_estendido.2026.21203, 2026)
Diabetic retinopathy (DR) is a leading cause of preventable blindness and a public health problem in Brazil, where access to ophthalmological specialists is often limited. Automated DR classification using deep learning (DL) can support screening programs, but deployment requires accurate, efficient, and scalable models. This work evaluates model selection and distributed scalability of convolutional neural networks (CNNs) for binary DR classification on ARM-based systems, using datasets from Federal University of São Paulo (UNIFESP), Hospital de Clínicas de Porto Alegre (HCPA), and TeleOftalmo (TelessaúdeRS-UFRGS). Among 38 evaluated CNN architectures, MobileNet performed best, consuming 77% less energy, training 83% faster, and producing an 85% smaller model than InceptionV3, while improving AUC by 3%. A preliminary distributed scalability evaluation across two NVIDIA Grace Superchips using data parallelism achieved a 1.93× speedup and 96.5% scaling efficiency. These results indicate that ARM-based distributed training is a feasible, energy-efficient option for scalable DR screening in Brazilian healthcare.