首页    期刊浏览 2024年07月08日 星期一
登录注册

文章基本信息

  • 标题:“Dr.J”: An Artificial Intelligence Powered Ultrasonography Breast Cancer Preliminary Screening Solution
  • 本地全文:下载
  • 作者:Zhenzhong Zhou ; Xueqin Xie ; Alex L. Zhou
  • 期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
  • 印刷版ISSN:2158-107X
  • 电子版ISSN:2156-5570
  • 出版年度:2020
  • 卷号:11
  • 期号:7
  • DOI:10.14569/IJACSA.2020.0110702
  • 出版社:Science and Information Society (SAI)
  • 摘要:Breast cancer ranks top incidence rate among all malignant tumors for women, globally. Early detection through regular preliminary screening is critical to decreasing the breast cancer’s fatality rate. However, the promotion of preliminary screening faces major limitations of human diagnosis capacity, cost, and technical reliability in China and most of the world. To meet these challenges, we developed a solution featuring an innovative division of labor model by incorporating artificial intelligence (AI) with ultrasonography and cloud computing. The objective of this research was to develop a solution named “Dr.J”, which applies AI to process real-time video live feed from ultrasonography, which is physically safe and more suitable for Asian women. It can automatically detect and highlight the suspected breast cancer lesions and provide BI-RADS (Breast Imaging-Reporting and Data System) ratings to assist human diagnosis. “Dr.J” does not require its frontline operators to have prior medical or IT background and thus significantly lowers manpower threshold for preliminary screening promotion. Furthermore, its cloud computing platform can store detailed breast cancer data such as images and BI-RADS ratings for further essential needs in medical treatment, research and health management, etc. as well as establishing a hierarchy medical service network for this disease. Therefore, “Dr.J” significantly enhances the availability and accessibility of preliminary screening service for breast cancer at grassroots.
  • 关键词:Breast cancer preliminary screening; lesions detection; ultrasonography; artificial intelligence; deep learning; cloud computing; BI-RADS
国家哲学社会科学文献中心版权所有