Abstract

Info

Title:: A diagnostic method incorporating multi-scale feature fusion and hybrid domain attention mechanism for fundus diseases

Author(s):: LIU Hui1; ZHU Zhengwei1; 2; ZHANG Xu1; ZHONG Hui1; 1. College of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, China 2. Robot?echnology?sed?or?pecial?nvironment?ey?aboratory?f?ichuan?rovince,?ianyang 621010,?hina

Keywords:: Keywords: fundus disease convolutional neural network multi-scale feature fusion hybrid domain attention mechanism

Abstract:: Abstract: In view of numerous subtle features in fundus disease images, small sample sizes, and difficulties in diagnosis, both deep learning and medical imaging technologies are used to develop a fundus disease diagnosis model that integrates multi-scale features and hybrid domain attention mechanism. Resnet50 network is taken as the baseline network, and it is modified in the study. The method uses parallel multi-branch architecture to extract the features of fundus diseases under different receptive fields for effectively improving the feature extraction ability and computational efficiency, and adopts hybrid domain attention mechanism to select information that is more critical to the current task for effectively enhancing the classification performance. The test on ODIR dataset shows that the proposed method has a diagnostic accuracy of 93.2% for different fundus diseases, which is 5.2% higher than the baseline network, demonstrating a good diagnostic performance.