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This paper proposes the importance of age and gender information in thediagnosis of diabetic retinopathy. We utilized Deep Residual Neural Networks(ResNet) and Densely Connected Convolutional Networks (DenseNet), which areproven effective on image classification problems and the diagnosis of diabeticretinopathy using the retinal fundus images. We used the ensemble of severalclassical networks and decentralized the training so that the network wassimple and avoided overfitting. To observe whether the age and genderinformation could help enhance the performance, we added the information beforethe dense layer and compared the results with the results that did not add ageand gender information. We found that the test accuracy of the network with ageand gender information was 2.67% higher than that of the network without ageand gender information. Meanwhile, compared with gender information, ageinformation had a better help for the results.

The Influence of Age and Gender Information on the Diagnosis of Diabetic Retinopathy: Based on Neural Networks