Performance of GAN-based augmentation for deep learning COVID-19 image classification

The biggest challenge in the application of deep learning to the medical domain is the availability of training data. Data augmentation is a typical methodology used in machine learning when confronted with a limited data set. In a classical approach image transformations i.e. rotations, cropping and brightness changes are used. In this work, a StyleGAN2-ADA model of Generative Adversarial Networks is trained on the limited COVID-19 chest X-ray image set. After assessing the quality of generated images they are used …

application augmentation availability challenge classification covid covid-19 cs.cv data data set deep learning domain eess.iv gan image machine machine learning medical methodology performance physics.med-ph set training training data