Retinal OCT image classification based on MGR-GAN

Accurately classifying optical coherence tomography (OCT) images is essential for diagnosing and treating ophthalmic diseases. This paper introduces a novel generative adversarial network framework called MGR-GAN. The masked image modeling (MIM) method is integrated into the GAN model's generator, enhancing its ability to synthesize more realistic images by reconstructing them based on unmasked patches. A ResNet-structured discriminator is employed to determine whether the image is generated by the generator. Through the unique game process of the generative adversarial network (GAN) model, the discriminator acquires high-level discriminant features, essential for precise OCT classification. Experimental results demonstrate that MGR-GAN achieves a classification accuracy of 98.4% on the original UCSD dataset. As the trained generator can synthesize OCT images with higher precision, and owing to category imbalances in the UCSD dataset, the generated OCT images are leveraged to address this imbalance. After balancing the UCSD dataset, the classification accuracy further improves to 99%.