Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation. (arXiv:2110.14160v2 [eess.IV] UPDATED)

Although deep learning based diabetic retinopathy (DR) classification methods
typically benefit from well-designed architectures of convolutional neural
networks, the training setting also has a non-negligible impact on the
prediction performance. The training setting includes various interdependent
components, such as objective function, data sampling strategy and data
augmentation approach. To identify the key components in a standard deep
learning framework (ResNet-50) for DR grading, we systematically analyze the
impact of several major components. Extensive experiments are conducted on a
publicly-available dataset …

arxiv components images investigation resnet resnet-50 the key