Category weighted network and relation weighted label for diabetic retinopathy screening

Diabetic retinopathy (DR) is the primary cause of blindness in adults. Incorporating machine learning into DR grading can improve the accuracy of medical diagnosis. However, problems, such as severe data imbalance, persists. Existing studies on DR grading ignore the correlation between its labels. In this study, a category weighted network (CWN) was proposed to achieve data balance at the model level. In the CWN, a reference for weight settings is provided by calculating the category gradient norm and reducing the experimental overhead. We proposed to use relation weighted labels instead of the one-hot label to investigate the distance relationship between labels. Experiments revealed that the proposed CWN achieved excellent performance on various DR datasets. Furthermore, relation weighted labels exhibit broad applicability and can improve other methods using one-hot labels. The proposed method achieved kappa scores of 0.9431 and 0.9226 and accuracy of 90.94% and 86.12% on DDR and APTOS datasets, respectively.

Automated summary

Diabetic retinopathy (DR) is a major cause of blindness in adults. Incorporating machine learning into DR grading can enhance medical diagnosis accuracy. This study proposes a category weighted network (CWN) to achieve data balance at the model level and introduces relation weighted labels to investigate label distance relationships. Experimental results demonstrate the excellent performance of the CWN on different DR datasets and the broad applicability of relation weighted labels in improving methods using one-hot labels. The proposed method achieves high kappa scores and accuracy on DDR and APTOS datasets.