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

Abstract

Purpose

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.

Results

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).

Conclusion

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

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Acknowledgements

For this study, Kazunori Kubota and Tomoyuki Fujioka received grant (KAKENHI-PROJECT-16K10270; https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-16K10270/).

Funding

Kazunori Kubota and Tomoyuki Fujioka received grant (KAKENHI-PROJECT-16K10270; https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-16K10270/).

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Correspondence to Mio Mori.

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

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

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Fujioka, T., Kubota, K., Mori, M. et al. Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network. Jpn J Radiol 37, 466–472 (2019). https://doi.org/10.1007/s11604-019-00831-5

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Keywords

  • Breast imaging
  • Ultrasound
  • Deep learning
  • Convolutional neural network
  • Artificial intelligence