The Journal of China Universities of Posts and Telecommunications ›› 2019, Vol. 26 ›› Issue (2): 9-16.doi: 10.19682/j.cnki.1005-8885.2019.1002

• Artificial intelligence • Previous Articles     Next Articles

Automatic detection of breast nodule in the ultrasound images using CNN

Pang Hao, Bu Yunyun, Wang Cong, Xiao Hui   

  1. 1. School of Software, Beijing University of Posts and Telecommunications, Beijing 100876, China
    2. Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing 100876, China
    3. Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University
    4. China Electronic Data Service Company Limited
  • Received:2018-08-07 Revised:2019-03-29 Online:2019-04-30 Published:2019-06-14
  • Contact: Corresponding author: Pang Hao, E-mail:
  • About author:Corresponding author: Pang Hao, E-mail:
  • Supported by:
    This work was supported by the National Key Research and Development Program of China (2016YFC0901602).

Abstract: Breast cancer is the most common cancer among women worldwide. Ultrasound is widely used as a harmless test for early breast cancer screening. The ultrasound network (USNet) model is presented. It is an improved object detection model specifically for breast nodule detection on ultrasound images. USNet improved the backbone network, optimized the generation of feature maps, and adjusted the loss function. Finally, USNet trained with real clinical data. The evaluation results show that the trained model has strong nodule detection ability. The mean average precision (mAP) value can reach 0.734 9. The nodule detection rate is 95.11%, and the in situ cancer detection rate is 79.65%. At the same time, detection speed can reach 27.3 frame per second (FPS), and the
video data can be processed in real time.

Key words: convolutional neural network, object detection, ultrasound, breast nodule

CLC Number: