Automatic detection of breast nodule in the ultrasound images using CNN

被引：0
|
作者：
Hao P. [1 ,2 ]
Yunyun B. [3 ]
Cong W. [1 ,2 ]
Hui X. [4 ]
机构：
[1] School of Software, Beijing University of Posts and Telecommunications, Beijing
[2] Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing
[3] Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University
来源：
Journal of China Universities of Posts and Telecommunications | 2019年 / 26卷 / 02期
关键词：
Breast nodule; Convolutional neural network; Object detection; Ultrasound;
D O I：
10.19682/j.cnki.1005-8885.2019.1002
中图分类号：
学科分类号：
摘要：
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. © 2019, Beijing University of Posts and Telecommunications. All rights reserved.
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页码：9 / 16
页数：7
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