Identifying the key components in ResNet-50 for diabetic retinopathy grading from fundus images: a systematic investigation

Yijin Huang, Li Lin, Pujin Cheng, Junyan Lyu, Xiaoying Tang

Keywords: Diabetic Retinopathy, Classification, Training Setting, ResNet-50

Abstract: 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 EyePACS. Based on our observations and an optimal combination of the investigated components, our framework, without any specialized network design, achieves the state-of-the-art result (0.8631 for Kappa) on the EyePACS test set (a total of 42670 fundus images) with only image-level labels. Our codes and pre-trained model are available at https://github.com/YijinHuang/pytorch-classification.

Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.

Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.

Paper Type: both

Primary Subject Area: Application: Ophthalmology

Secondary Subject Area: Detection and Diagnosis

Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2110.14160/code)