Classification of whole mammogram and tomosynthesis images using deep convolutional neural networks

Xiaofei Zhang, Yi Zhang, Erik Y. Han, Nathan Jacobs, Qiong Han, Xiaoqin Wang, Jinze Liu

Research output: Contribution to journalArticlepeer-review

70 Scopus citations


Mammography is the most popular technology used for the early detection of breast cancer. Manual classification of mammogram images is a hard task because of the variability of the tumor. It yields a noteworthy number of patients being called back to perform biopsies, ensuring no missing diagnosis. The convolutional neural network (CNN) has succeeded in a lot of image classification challenges during the recent years. In this paper, we proposed an approach of mammogram and tomosynthesis classification based on CNNs. We had acquired more than 3000 mammograms and tomosynthesis data with approval from an institutional review board at the University of Kentucky. Different models of CNNs were built to classify both the 2-D mammograms and 3-D tomosynthesis, and every classifier was assessed with respect to truth-values generated by histology results from the biopsy and two-year negative mammogram follow-up confirmed by expert radiologists. Our outcomes demonstrated that CNN-based models we had built and optimized utilizing transfer learning and data augmentation have good potential for automatic breast cancer detection based on the mammograms and tomosynthesis data.

Original languageEnglish
Article number8374855
Pages (from-to)237-242
Number of pages6
JournalIEEE Transactions on Nanobioscience
Issue number3
StatePublished - Jul 2018

Bibliographical note

Funding Information:
Manuscript received May 16, 2018; accepted May 16, 2018. Date of publication June 7, 2018; date of current version July 31, 2018. The work of N. Jacobs was supported by NSF CAREER grant IIS-1553116, the work of X. Wang and J. Liu was supported by Grant IRG 16-182-28 from the American Cancer Society, and the work of J. Liu was supported by NIH Grants P30 CA177558 and UL1TR001998. (Corresponding authors: Xiaoqin Wang; Jinze Liu.) X. Zhang, Y. Zhang, N. Jacobs, and J. Liu are with the Department of Computer Science, University of Kentucky, Lexington, KY 40506 USA (e-mail:

Publisher Copyright:
© 2018 IEEE.


  • Mammogram
  • classification
  • convolutional neural network
  • tomosynthesis

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Medicine (miscellaneous)
  • Biomedical Engineering
  • Pharmaceutical Science
  • Computer Science Applications
  • Electrical and Electronic Engineering


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