RDA-UNET-WGAN: An Accurate Breast Ultrasound Lesion Segmentation Using Wasserstein Generative Adversarial Networks

Abstract

Early-stage detection of lesions is the best possible way to fight breast cancer, a disease with the highest malignancy ratio among women. Though several methods primarily based on deep learning have been proposed for tumor segmentation, it is still a challenging problem due to false positives and the precise boundary detection required for segmentation. In this paper, we propose a Generative Adversarial Network (GAN) based algorithm for segmenting the tumor in Breast Ultrasound images. The GAN model comprises of two modules: generator and discriminator. Residual-Dilated-Attention-Gate-UNet (RDAU-NET) is used as the generator which serves as a segmentation module and a CNN classifier is employed as the discriminator. To stabilize training, Wasserstein GAN (WGAN) algorithm has been used. The proposed hybrid deep learning model is called the WGAN-RDA-UNET. The model is assessed with several quantitative metrics and is also compared with existing methods both quantitatively and qualitatively. The overall Accuracy, PR-AUC, ROC-AUC and F1-score achieved were 0.98, 0.95, 0.89 and 0.88 respectively which are better than most conventional deep net models. The results also showcase the shortcomings of CNN, RDA U-Net and other models and how they can be rectified using the WGAN-RDA-UNET model.