Deep Learning-based Multi-stage segmentation method using ultrasound images for breast cancer diagnosis

Globally, breast cancer occurs frequently in women and has the highest mortality rate. Owing to the increased need for a rapid and reliable initial diagnosis of breast cancer, several breast tumor segmentation methods based on ultrasound images have attracted research attention. Most conventional methods use a single network and demonstrate high performance by accurately classifying tumor-containing and normal image pixels. However, tests performed using normal images have revealed the occurrence of many false-positive errors. To address this limitation, this study proposes a multistage-based breast tumor segmentation technique based on the classification and segmentation of ultrasound images. In our method, a breast tumor ensemble classification network (BTEC-Net) is designed to classify whether an ultrasound image contains breast tumors or not. In the segmentation stage, a residual feature selection UNet (RFS-UNet) is used to exclusively segment images classified as abnormal by the BTEC-Net. The proposed multistage segmentation method can be adopted as a fully automated diagnosis system because it can classify images as tumor-containing or normal and effectively specify the breast tumor regions.