Ultrasound breast cancer image classification with GAN-based synthetic data augmentation

Ultrasound images are used in various branches of medicine to detect diseases. The process of obtaining this data is complex due to procedures and legal restrictions, leading to scarce datasets. Different data augmentation techniques can be employed to improve classification performance. This paper shows that augmenting the ultrasound breast cancer images dataset using generative adversarial networks (GANs) increased the classification accuracy compared to the original dataset and compared to the dataset augmented using standard techniques.