Classification of Optical Coherence Tomography Images Using Generative Adversarial Networks

The retina may be affected by many diseases such as Age-related Macular Degeneration (AMD), Diabetic Macular Disease (DME), and Choroidal Neovascularization (CNV). To diagnose these diseases, one way is to analyze retinal Optical Coherence Tomography (OCT) images using image processing algorithms. In this paper, a novel architecture based on Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) is proposed to classify OCT images with insufficient training samples. The proposed method generates OCT images with pseudo labels from the distribution of original OCT images. OCT samples with real and pseudo labels are presented to a CNN classifier which leads to a model which is robust against insufficient samples. UCSD dataset has been used to evaluate the proposed method. Results indicate that the proposed method is comparable to the state-of-the-art methods in the terms of Precision, Sensitivity, Specificity, and F-Measure. Source code of this paper is available online at Github (https://github.com/mohamad-sw/oct-image-classification-using-gans).