| JIANG Jiewei,GUO Liufei,LIU Wei,WU Chengchao,GONG Jiamin,LI Zhongwen.Automatic diagnosis of multiple fundus lesions based on depth graph neural network[J].Optoelectronics Letters,2023,(5):307-315 |
| Automatic diagnosis of multiple fundus lesions based on depth graph neural network |
| Author Name | Affiliation | | JIANG Jiewei | School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710071, China | | GUO Liufei | School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710071, China | | LIU Wei | School of Communication Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710071, China | | WU Chengchao | School of Communication Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710071, China | | GONG Jiamin | School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710071, China
School of Communication Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710071, China | | LI Zhongwen | Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China |
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| Abstract: |
| Fundus images are commonly used to capture changes in fundus structures and the severity of fundus lesions, and are the basis for detecting and treating ophthalmic diseases as well as other important diseases. This study proposes an automatic diagnosis method for multiple fundus lesions based on a deep graph neural network (GNN). 2 083 fundus images were collected and annotated to develop and evaluate the performance of the algorithm. First, high-level semantic features of fundus images are extracted using deep convolutional neural networks (CNNs). Then the features are input into the GNN to model the correlation between different lesions by mining and learning the correlation between lesions. Finally, the input and output features of the GNN are fused, and a multi-label classifier is used to complete the automatic diagnosis of fundus lesions. Experimental results show that the method proposed in this study can learn the correlations between lesions to improve the diagnostic performance of the algorithm, achieving better performance than the original ResNet and DenseNet models in both qualitative and quantitative evaluation. |
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