Diabetic retinopathy fundus image generation based on generative adversarial networks

Abstract

Objective To generate various types of diabetic retinopathy (DR) fundus images automatically by computer vision algorithm. Methods A method based on deep learning to generate fundus images was proposed, which used the vascular vein of the fundus image and the text description of lesions as the constraint conditions to generate fundus image.The text description was encoded by using a long short-term memory (LSTM), and the vascular vein image was encoded by a convolutional neural network (CNN). Then the encoded information was combined and used to generate a fundus image by generative adversarial networks (GAN). Results The results showed that the algorithm can generate realistic fundus images.However, the image detail features were not obvious because the text-encoded recurrent neural network (RNN) loss function did not converge well. Conclusions Using the GAN can generate realistic DR fundus images, which has certain application value in expanding medical data.However, the generation of detail features in small areas still needs improvement. Key words: Generative adversarial networks; Fundus images; Image generation; Convolutional neural network