Skip to main content

Building a X-ray Database for Mammography on Vietnamese Patients and automatic Detecting ROI Using Mask-RCNN

Part of the Studies in Computational Intelligence book series (SCI,volume 899)


This paper describes the method of building a X-ray database for Mammography on Vietnamese patients that we collected at Hanoi Medical University Hospital. This dataset has 4664 images (Dicom) corresponding to 1161 standard patients with uniform distribution according to BIRAD from 0 to 5. This paper also presents the method of detecting Region of Interest (ROI) in mammogram based on Mask R-CNN architecture. The method of determining the ROI for accuracy mAP@0.5 = 0.8109 and the accuracy of classification BIRAD levels is 58.44%.

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-49536-7_27
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   139.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-49536-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   179.99
Price excludes VAT (USA)
Hardcover Book
USD   179.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14


  1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G., Davis, A., Dean, J., Devin, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems (2015)

    Google Scholar 

  2. Agarwal, R., Diaz, O., Llado, X., Yap, M.H., Mart, R.: Automatic mass detection in mammograms using deep convolutional neural networks. J. Med. Imaging 6, 1 (2019)

    CrossRef  Google Scholar 

  3. Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., Warde-Farley, D., Bengio, Y.: Theano: new features and speed improvements. arXiv preprint arXiv:1211.5590 (2012)

  4. Chollet, F., et al.: Keras (2015)

    Google Scholar 

  5. Collobert, R., Kavukcuoglu, K., Farabet, C.: Torch7: a matlab-like environment for machine learning. In: BigLearn, NIPS workshop, number EPFL-CONF-192376 (2011)

    Google Scholar 

  6. Fukushima, K., Miyake, S.: Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition. In Competition and Cooperation in Neural Nets, pp. 267–285. Springer (1982)

    Google Scholar 

  7. Gulshan, V., Peng, L., Coram, M., Stumpe, M.C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. J. Am. Med. Assoc. 316, 2402–2410 (2016)

    CrossRef  Google Scholar 

  8. He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), October 2017

    Google Scholar 

  9. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  10. Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \(<\)0.5 mb model size. arXiv preprint arXiv:1602.07360 (2016)

  11. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multi- media, pp. 675–678. ACM (2014)

    Google Scholar 

  12. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  13. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    CrossRef  Google Scholar 

  14. LeCun, Y., et al.: LeNet-5, convolutional neural networks, p. 20 (2015).

  15. Lo, S.-C.B., Lou, S.-L.A., Lin, J.-S., Freedman, M.T., Chien, M.V., Mun, S.K.: Artficial convolution neural network techniques and applications for lung nodule detection. IEEE Trans. Med. Imaging 14, 711–718 (1995)

    CrossRef  Google Scholar 

  16. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

    CrossRef  Google Scholar 

  17. Shen, L., Margolies, L.R., Rothstein, J.H., Fluder, E., McBride, R.B., Sieh, W.: Deep learning to improve breast cancer early detection on screening mammography (2017)

    Google Scholar 

  18. Tsochatzidis, L., Costaridou, L., Pratikakis, I.: Deep learning for breast cancer diagnosis from mammograms - a comparative study. J. Imaging 5(3), 37 (2019)

    CrossRef  Google Scholar 

  19. Wu, N., Phang, J., Park, J., Shen, Y., Huang, Z., Zorin, M., Jastrzebski, S., Fevry, T., Katsnelson, J., Kim, E., et al.: Deep neural networks improve radiologists performance in breast cancer screening. IEEE J. Med. Imaging 39, 1184–1194 (2020)

    Google Scholar 

  20. Tung, T.: Each year there are 11,000 cases of breast cancer in Vietnam.

  21. Trieu, P.D.Y., Mello-Thoms, C., Brennan, P.C.: Female breast cancer in Vietnam: a comparison across Asian specific regions. Cancer Biol. Med.

  22. DDSM: Digital Database for Screening Mammography.

  23. Curated Breast Imaging Subset of DDSM.

  24. Mammographic image analysis homepage.

  25. Wu, N., et al.: The NYU breast cancer screening dataset v1.0, Technical report (2019).

Download references


This work is supported by foundation of the research and development contract between Thang Long University and Hanoi Medical University Hospital, Vietnam dated on 27 November, 2018 on “Developing a support system for diagnosis of breast cancer based on X-Ray using Artificial Intelligence”.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Nguyen Hoang Phuong .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Thang, N.D. et al. (2021). Building a X-ray Database for Mammography on Vietnamese Patients and automatic Detecting ROI Using Mask-RCNN. In: Kreinovich, V., Hoang Phuong, N. (eds) Soft Computing for Biomedical Applications and Related Topics. Studies in Computational Intelligence, vol 899. Springer, Cham.

Download citation