Grading of diabetic retinopathy images based on capsule networks and graph neural network
Diabetic Retinopathy(DR)has become one of the main reasons for the rise in the number of blind people worldwide.At present,the intelligent grading based on deep learning has become a hotspot in DR intelligent diagnosis,The existing DR Intelligent classification models based on convolutional neural networks(CNN)have achieved good results,these models regard DR classification as images classification task,but these models pay more attention on the extraction of deep features,and do not consider the characteristics of the DR image itself,the relationships between different levels,etc.In order to overcome these shortcomings,a new DR intelligent classification model is proposed.First,by using the detail capturing capability of capsule network,the pooling layer of CNN is replaced by capsule network to extract the deep detail features of DR images.Secondly,considering the small difference of DR images between adjacent levels,which is easy to be confused,the graph neural network is used to capture the relationship between DR levels.Finally,the output of the two networks is fused through the adaptive weight,and give the classification results of the whole network.The proposed model is evaluated on two datasets respectively,and good results are obtained,which further demonstrates the superiority of the proposed method.
NETL
NSTL
万方数据
