Japanese Journal of Radiology

, Volume 37, Issue 6, pp 466–472 | Cite as

Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network

  • Tomoyuki Fujioka
  • Kazunori Kubota
  • Mio MoriEmail author
  • Yuka Kikuchi
  • Leona Katsuta
  • Mai Kasahara
  • Goshi Oda
  • Toshiyuki Ishiba
  • Tsuyoshi Nakagawa
  • Ukihide Tateishi
Original Article



We aimed to use deep learning with convolutional neural network (CNN) to discriminate between benign and malignant breast mass images from ultrasound.

Materials and Methods

We retrospectively gathered 480 images of 96 benign masses and 467 images of 144 malignant masses for training data. Deep learning model was constructed using CNN architecture GoogLeNet and analyzed test data: 48 benign masses, 72 malignant masses. Three radiologists interpreted these test data. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated.


The CNN model and radiologists had a sensitivity of 0.958 and 0.583–0.917, specificity of 0.925 and 0.604–0.771, and accuracy of 0.925 and 0.658–0.792, respectively. The CNN model had equal or better diagnostic performance compared to radiologists (AUC = 0.913 and 0.728–0.845, p = 0.01–0.14).


Deep learning with CNN shows high diagnostic performance to discriminate between benign and malignant breast masses on ultrasound.


Breast imaging Ultrasound Deep learning Convolutional neural network Artificial intelligence 



For this study, Kazunori Kubota and Tomoyuki Fujioka received grant (KAKENHI-PROJECT-16K10270;


Kazunori Kubota and Tomoyuki Fujioka received grant (KAKENHI-PROJECT-16K10270;

Compliance with ethical standards

Conflict of interest

All the authors and their institution have no conflicts of interest.

Ethical approval

All procedures used in this research were approved by the Ethical Committee of Tokyo Medical and Dental University, Medical Hospital.


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Copyright information

© Japan Radiological Society 2019

Authors and Affiliations

  1. 1.Department of RadiologyTokyo Medical and Dental UniversityTokyoJapan
  2. 2.Department of Surgery, Breast SurgeryTokyo Medical and Dental UniversityTokyoJapan

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