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Diagnostic Efficiency of the Breast Ultrasound Computer-Aided Prediction Model Based on Convolutional Neural Network in Breast Cancer

Abstract

This study aimed to construct a breast ultrasound computer-aided prediction model based on the convolutional neural network (CNN) and investigate its diagnostic efficiency in breast cancer. A retrospective analysis was carried out, including 5000 breast ultrasound images (benign: 2500; malignant: 2500) as the training group. Different prediction models were constructed using CNN (based on InceptionV3, VGG16, ResNet50, and VGG19). Additionally, the constructed prediction models were tested using 1007 images of the test group (benign: 788; malignant: 219). The receiver operating characteristic curves were drawn, and the corresponding areas under the curve (AUCs) were obtained. The model with the highest AUC was selected, and its diagnostic accuracy was compared with that obtained by sonographers who performed and interpreted ultrasonographic examinations using 683 images of the comparison group (benign: 493; malignant: 190). In the model test with the test group images, the AUCs of the constructed InceptionV3, VGG16, ResNet50, and VGG19 models were 0.905, 0.866, 0.851, and 0.847, respectively. The InceptionV3 model showed the largest AUC, with statistically significant differences compared with the other models (P < 0.05). In the classification of the comparison group images, the AUC (0.913) of the InceptionV3 model was larger than that (0.846) obtained by sonographers, showing a statistically significant difference (P < 0.05). The breast ultrasound computer-aided prediction model based on CNN showed high accuracy in the prediction of breast cancer.

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References

  1. 1.

    Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68: 394–424,2018

    Google Scholar 

  2. 2.

    Sadoughi F, Kazemy Z, Hamedan F, Owji L, Rahmanikatigari M, Azadboni TT: Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review. Breast Cancer (Dove Med Press) 10: 219–230,2018

    Google Scholar 

  3. 3.

    Abdel-Zaher AM, Eldeib AM: Breast cancer classification using deep belief networks. Expert Systems with Applications 46:139–144,2016

    Article  Google Scholar 

  4. 4.

    Seung Yeon S, Soochahn L, Il Dong Y, Sun Mi K, Kyoung Mu L: Joint weakly and semi-supervised deep learning for localization and classification of masses in breast ultrasound images. IEEE Trans Med Imaging 38:762–774,2019

    Article  Google Scholar 

  5. 5.

    Fujioka T, Kubota K, Mori M, Kikuchi Y, Katsuta L, Kasahara 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

    Article  Google Scholar 

  6. 6.

    Pehrson LM, Lauridsen C, Nielsen MB: Machine learning and deep learning applied in ultrasound. Ultraschall Med 39:379–381,2018

    Article  Google Scholar 

  7. 7.

    Byra M, Galperin M, Ojeda-Fournier H, Olson L, O'Boyle M, Comstock C, et al.: Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion. Med Phys 46:746–755,2019

    Article  Google Scholar 

  8. 8.

    Mendelson E, Böhm-Vélez M, Berg W: ACR BI-RADS® Ultrasound. In: ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System. Reston: American College of Radiology, 2013

    Google Scholar 

  9. 9.

    He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K: The practical implementation of artificial intelligence technologies in medicine. Nat Med 25:30–36,2019

    CAS  Article  Google Scholar 

  10. 10.

    Xiao T, Liu L, Li K, Qin W, Yu S, Li Z: Comparison of transferred deep neural networks in ultrasonic breast masses discrimination. Biomed Res Int 2018:4605191,2018

    Google Scholar 

  11. 11.

    Becker AS, Mueller M, Stoffel E, Marcon M, Ghafoor S, Boss A: Classification of breast cancer in ultrasound imaging using a generic deep learning analysis software: a pilot study. Br J Radiol 91:20170576,2018

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Han S, Kang HK, Jeong JY, Park MH, Kim W, Bang WC, et al.: A deep learning framework for supporting the classification of breast lesions in ultrasound images. Phys Med Biol 62:7714–7728,2017

    Article  Google Scholar 

Download references

Acknowledgments

We gratefully acknowledge the kind cooperation of Haihong Intellimage Medical Technology (Tianjin) Co., Ltd., in terms of software and technical service.

Funding

This work was supported by the Achievement Conversion and Guidance Project of Chengdu Science and Technology Bureau (No.2017-CY02-00027-GX).

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Correspondence to Yulan Peng.

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The authors declare that they have no conflict of interest.

Ethical Approval Retrospective Studies

This study was approved by the ethics committee of the relevant institutions.

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Informed consent was obtained from all individual participants included in the study.

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Zhang, H., Han, L., Chen, K. et al. Diagnostic Efficiency of the Breast Ultrasound Computer-Aided Prediction Model Based on Convolutional Neural Network in Breast Cancer. J Digit Imaging 33, 1218–1223 (2020). https://doi.org/10.1007/s10278-020-00357-7

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Keywords

  • Breast cancer
  • Computer prediction model
  • Convolutional neural network
  • Diagnosis
  • Ultrasound