Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms.

Journal: Computational and mathematical methods in medicine
Published Date: Jan 1, 2019

Abstract

Mammography is successfully used as an effective screening tool for cancer diagnosis. A calcification cluster on mammography is a primary sign of cancer. Early researches have proved the diagnostic value of the calcification, yet their performance is highly dependent on handcrafted image descriptors. Characterizing the calcification mammography in an automatic and robust way remains a challenge. In this paper, the calcification was characterized by descriptors obtained from deep learning and handcrafted descriptors. We compared the performances of different image feature sets on digital mammograms. The feature sets included the deep features alone, the handcrafted features, their combination, and the filtered deep features. Experimental results have demonstrated that the deep features outperform handcrafted features, but the handcrafted features can provide complementary information for deep features. We achieved a classification precision of 89.32% and sensitivity of 86.89% using the filtered deep features, which is the best performance among all the feature sets.

Authors

  • Hongmin Cai
    School of Computer Science& Engineering, South China University of Technology, Guangdong, China. hmcai@scut.edu.cn.
  • Qinjian Huang
    School of Computer Science and Engineering, South China University of Technology, Guangzhou 510000, China.
  • Wentao Rong
    School of Computer Science and Engineering, South China University of Technology, Guangzhou 510000, China.
  • Yan Song
    Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
  • Jiao Li
    CAS Key Laboratory of Tropical Marine Bio-resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences Guangzhou 510301 China yinhao@scsio.ac.cn.
  • Jinhua Wang
    Department of Pharmacy Intravenous Admixture Service, the First Affiliated Hospital of Harbin Medical University, Harbin, P. R., China.
  • Jiazhou Chen
    School of Computer Science and Engineering, South China University of Technology, Guangzhou 510000, China.
  • Li Li
    Department of Gastric Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.

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