A DR Detection Method Incorporating Multi-scale Techniques and Parallel Networks - CHEN Yu; XU Shibao (Journal of Harbin University of Science and Technology)
A PM-Net model ( Parallel Multi-scale Network) has been constructed to solve the problems of loss of key information and poor model robustness in the downsampling process of diabetic retinopathy ( DR) detection models. Multi-scale maximum pooling and multi-scale convolution modules have been designed and improved on ResNet-50 using information augmentation in the downsampling process. In addition，to improve the robustness of the model，the model was extended using a two-branch architecture. The proposed multi-scale module allows the model to obtain richer retinal fundus image features during downsampling，thus improving DR detection performance ， while the proposed two-branch model ensures the robustness of the model with local information supplementing global information during DR detection. The model was experimentally validated on EyePACS ，DDR , and private datasets. Experimental results show that the model accuracy and quadratic weighted kappa score on the EyePACS dataset are improved by 2. 58% and 1. 31% respectively compared to mainstream models.