Automatic Diagnosis of Different Types of Retinal Vein Occlusion Based on Fundus Images (Preprint)

BACKGROUND Retinal vein occlusion (RVO) is the second common cause of blindness following diabetic retinopathy. Patients with RVO often develop macular edema and neovascular glaucoma, which may damage the visual function irreversibly. RVO includes macular retinal vein occlusion, central retinal vein occlusion, and branch retinal vein occlusion. OBJECTIVE Providing patients with an accurate diagnosis, followed by timely and effective treatment is very important for the prognosis of visual function. Therefore, in this paper, we use the Swin Transformer model with a label smoothing method to identify fundus images. METHODS First, 483 and 161 fundus images were used as the training set and the validation set, respectively, to train and regulate the model, whose accuracy reached 98.1%. Additional 161 fundus images were used as the test set to evaluate the model's performance. Next, the area under the receiver operating curve corresponding to macular retinal vein occlusion, central retinal vein occlusion, and branch retinal vein occlusion were obtained using the Swin Transformer model. Finally, we compared the results using the model trained by the deep convolutional neural network. RESULTS The values obtained using the Swin Transformer model for macular retinal vein occlusion, central retinal vein occlusion, and branch retinal vein occlusion were 0.9987, 0.9981, and 0.9974, respectively. The comparison results with other models indicated that the Swin Transformer model performed the best. The results of the study demonstrated that our method can automatically diagnose RVO and determine the type through fundus images, which has the potency to help in the early diagnosis of patients with RVO. CONCLUSIONS Our model can automatically diagnose RVO through fundus images, and its diagnostic accuracy is higher than that of MobileNetV2 and ResNet18. In addition, it can process data sets automatically and efficiently without manual assistance. We can not only diagnose RVO, but also accurately judge its specific type, which has an important clinical significance in real life.