Abstract: Diabetic retinopathy is a common complication of diabetes,and accurately identifying the stages of diabetic retinopathy is crucial for subsequent treatment.Fundus images play a key role in the grading of diabetic retinopathy.With the advancement of artificial intelligence technologies,many researchers have extracted deep features and radiomic features from fundus images to conduct studies on the grading of diabetic retinopathy.This study combines deep features and radiomic features to design a feature fusion algorithm.Firstly,deep features are extracted from fundus images using convolutional neural networks,while radiomic features are obtained through radiomic methods.Subsequently,a label relaxation-based multi-view learning algorithm is designed for feature fusion.The primary goal of label relaxation is to enhance the distinguishability of training samples in the label space,thereby improving the classification accuracy of the model.Furthermore,this study introduces a graph constraint based on manifold learning methods to mitigate the overfitting issues caused by label relaxation.Finally,theeffectiveness of the proposed methodis validated on two fundus image datasets:the DR1 dataset and the MESSIDOR dataset.

Key words: Diabetic retinopathy grading, Multi-view feature fusion, Radiomics, Deep features, Label relaxation