Breast Ultrasound Image Detection Based on Dual-Branch Faster R-CNN
C. Liu, Bo Yang, Lijuan Zhang, Lijun Liu, Wenfeng Zheng, Lijun Liu, Wenfeng Zheng
Abstract
This paper proposes a novel dual-branch Faster R-CNN model, termed D-faster R-CNN, for breast ultrasound image detection. It adds a new parallel backbone, Pyramid Vision Transformer (PVT), to the original ResNet50 backbone, forming a dual-branch feature extraction structure of ResNet50 and PVT. To enhance the extracted features on the PVT and ResNet50 branches, a simple feature pyramid network and an asymptotic feature pyramid network are used, respectively, and the enhanced features are merged for the subsequent region proposal and RoI Align. The proposed model is validated on a publicly available breast ultrasound image dataset (BUSI). The experimental results show that compared with the baseline models, the proposed D-faster R-CNN with dual-feature extraction backbone can effectively improve tumor detection performance, and the average precision is significantly improved.