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In order to speed up the diagnosis of DR, a mass of deep learning methods have been used for the detection of this disease, but they failed to attain excellent results due to unbalanced training data, i.e., the lack of DR fundus images. To address the problem of data imbalance, this paper proposes a method dubbed retinal fundus images generative adversarial networks (RF\u2010GANs), which is based on generative adversarial network, to synthesize retinal fundus images. RF\u2010GANs is composed of two generation models, RF\u2010GAN1 and RF\u2010GAN2. Firstly, RF\u2010GAN1 is employed to translate retinal fundus images from source domain (the domain of semantic segmentation datasets) to target domain (the domain of EyePACS dataset connected to Kaggle (EyePACS)). Then, we train the semantic segmentation models with the translated images, and employ the trained models to extract the structural and lesion masks (hereafter, we refer to it as Masks) of EyePACS. Finally, we employ RF\u2010GAN2 to synthesize retinal fundus images using the Masks and DR grading labels. This paper verifies the effectiveness of the method: RF\u2010GAN1 can narrow down the domain gap between different datasets to improve the performance of the segmentation models. RF\u2010GAN2 can synthesize realistic retinal fundus images. Adopting the synthesized images for data augmentation, the accuracy and quadratic weighted kappa of the state\u2010of\u2010the\u2010art DR grading model on the testing set of EyePACS increase by 1.53% and 1.70%, respectively.<\/jats:p>","DOI":"10.1155\/2021\/3812865","type":"journal-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T05:50:10Z","timestamp":1636609810000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["RF\u2010GANs: A Method to Synthesize Retinal Fundus Images Based on Generative Adversarial Network"],"prefix":"10.1155","volume":"2021","author":[{"given":"Yu","family":"Chen","sequence":"first","affiliation":[]},{"given":"Jun","family":"Long","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8692-6255","authenticated-orcid":false,"given":"Jifeng","family":"Guo","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,11,10]]},"reference":[{"key":"e_1_2_10_1_2","volume-title":"Ophthalmoscopy, Dilated and E. 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