Classification of fundus diseases based on meta-data and EB-IRV2 network

Abstract

Aiming at the problem that there may be one or more diseases and unbalanced distribution of labels in fundus images, in this paper proposes a multi-label classification method for fundus diseases based on the fusion of meta-data and EB-IRV2 network. Firstly, Efficientnet-B2 and InceptionResNetV2 networks are used to extract feature information from the left and right fundus image data, and then fuse with the meta-data with patient information, finally send them to the classifier for multi-label classification of fundus diseases. Adding patient’s meta-information into the model helps to better capture the lesion information and the location of the lesion in the fundus image, thus improving the accuracy of recognition. The experimental results show that the model in this paper achieves good classification results on the ODIR fundus image database, the accuracy rate is 96.00%, the recall rate is 92.37% and the F1-score is 94.11%, indicating that the proposed model has good robustness in the classification of multi-labeled fundus images.