Diabetic retinopathy (DR) and diabetic macular edema (DME) are the major causes of blindness in humans. This study proposes to leverage the ResNeSt network to focus on the close relationship between DR and DME, and use it to achieve their joint grading to improve grading accuracy. Specifically, common features and unique features of DR and DME are extracted by using different stages of ResNeSt. Subsequently, a mutual attention module for feature fusion is employed to realize their respective grading. On the Messidor dataset, by using both ResNeSt network and the mutual attention module, the grading accuracies of DR and DME were 95.6% and 95.0%, respectively, and the joint accuracy was 86.7%. In contrast, when only the ResNeSt network was used, the grading accuracies of the two were 95.0% and 90.8%, respectively. On the IDRiD dataset, the joint accuracy of the two reached 66.3%. The results on the datasets indicate that the proposed joint grading of DR and DME based on the ResNeSt network can improve grading accuracy.
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