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

TL;DR

This study investigates how incorporating age and gender data into neural network models improves the accuracy of diabetic retinopathy diagnosis from retinal images, demonstrating a notable performance boost.

Contribution

The paper introduces a method of integrating demographic information into deep learning models for diabetic retinopathy detection, showing improved accuracy over models without such data.

Findings

Adding age and gender increases test accuracy by 2.67%.

Age information contributes more to performance improvement than gender.

Ensemble of classical networks with demographic data enhances diagnosis accuracy.

Abstract

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