Grading the severity of diabetic retinopathy using an ensemble of self-supervised pre-trained convolutional neural networks: ESSP-CNNs

The proposed ESSP-CNNs framework employs self-supervised learning, specifically the Bootstrap Your Own Latent (BYOL) technique, to pre-train neural networks on a vast unlabeled dataset, and employs deep ensemble learning to construct a robust model for DR grading.