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Breast Cancer Detection and Localization Using MobileNet Based Transfer Learning for Mammograms

  • Wajeeha Ansar
  • Ahmad Raza Shahid
  • Basit RazaEmail author
  • Amir Hanif Dar
Conference paper
  • 48 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1187)

Abstract

Breast cancer is the major cause of death among women. The best and most efficient approach for controlling cancer progression is early detection and diagnosis. As opposed to biopsy, mammography helps in early detection of cancer and hence saves lives. Mass classification in mammograms remains a major challenge and plays a vital role in helping radiologists in accurate diagnosis. In this work, we propose a MobileNet based architecture for early breast cancer detection and further classify mass into malignant and benign. It requires less memory space and provides faster computations with 86.8% and 74.5% accurate results for DDSM and CBIS-DDSM, respectively. We have achieved better results than other deep CNN models such as AlexNet, VGG16, GoogleNet, and ResNet.

Keywords

Breast cancer Mammography Deep learning MobileNet Localization 

Notes

Acknowledgement

This work has been supported by Higher Education Commission under Grant # 2(1064) and is carried out at Medical Imaging and Diagnostics (MID) Lab at COMSATS University Islamabad, under the umbrella of National Center of Artificial Intelligence (NCAI), Pakistan.

References

  1. 1.
    Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2019. CA Cancer J. Clin. 69, 7–34 (2019)CrossRefGoogle Scholar
  2. 2.
    Feig, S.A.: Screening mammography benefit controversies: sorting the evidence. Radiol. Clin. 52, 455–480 (2014)CrossRefGoogle Scholar
  3. 3.
    Welch, H.G., Passow, H.J.: Quantifying the benefits and harms of screening mammography. JAMA Internal Med. 174, 448–454 (2014)CrossRefGoogle Scholar
  4. 4.
    Lee, J.-G., et al.: Deep learning in medical imaging: general overview. Korean J. Radiol. 18, 570–584 (2017)CrossRefGoogle Scholar
  5. 5.
    Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
  6. 6.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  7. 7.
    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
  8. 8.
    Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  9. 9.
    Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)Google Scholar
  10. 10.
    Jiang, F., Liu, H., Yu, S., Xie, Y.: Breast mass lesion classification in mammograms by transfer learning. In: Proceedings of the 5th International Conference on Bioinformatics and Computational Biology, pp. 59–62 (2017)Google Scholar
  11. 11.
    Huynh, B.Q., Li, H., Giger, M.L.: Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J. Med. Imaging 3, 034501 (2016)CrossRefGoogle Scholar
  12. 12.
    Rampun, A., Scotney, B.W., Morrow, P.J., Wang, H.: Breast mass classification in mammograms using ensemble convolutional neural networks. In: 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 1–6 (2018)Google Scholar
  13. 13.
    Hamidinekoo, A., Suhail, Z., Denton, E., Zwiggelaar, R.: Comparing the performance of various deep networks for binary classification of breast tumours. In: 14th International Workshop on Breast Imaging (IWBI 2018), p. 1071807 (2018)Google Scholar
  14. 14.
    Chougrad, H., Zouaki, H., Alheyane, O.: Deep convolutional neural networks for breast cancer screening. Comput. Methods Programs Biomed. 157, 19–30 (2018)CrossRefGoogle Scholar
  15. 15.
    Morrell, S., Wojna, Z., Khoo, C.S., Ourselin, S., Iglesias, J.E.: Large-scale mammography cad with deformable conv-nets. In: Stoyanov, D., et al. (eds.) RAMBO/BIA/TIA 2018. LNCS, vol. 11040, pp. 64–72. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-00946-5_7CrossRefGoogle Scholar
  16. 16.
    Carneiro, G., Nascimento, J., Bradley, A.P.: Deep learning models for classifying mammogram exams containing unregistered multi-view images and segmentation maps of lesions. In: Zhou, S.K., Greenspan, H., Shen, D. (eds.) Deep Learning for Medical Image Analysis, pp. 321–339. Elsevier, Amsterdam (2017)CrossRefGoogle Scholar
  17. 17.
    Carneiro, G., Nascimento, J., Bradley, A.P.: Unregistered multiview mammogram analysis with pre-trained deep learning models. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 652–660 (2015)Google Scholar
  18. 18.
    Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, W.P.: The digital database for screening mammography. In: Proceedings of the 5th International Workshop on Digital Mammography, pp. 212–218 (2000)Google Scholar
  19. 19.
    Lee, R.S., Gimenez, F., Hoogi, A., Miyake, K.K., Gorovoy, M., Rubin, D.L.: A curated mammography data set for use in computer-aided detection and diagnosis research. Sci. Data 4, 170177 (2017)CrossRefGoogle Scholar
  20. 20.
    Teare, P., Fishman, M., Benzaquen, O., Toledano, E., Elnekave, E.: Malignancy detection on mammography using dual deep convolutional neural networks and genetically discovered false color input enhancement. J. Digit. Imaging 30, 499–505 (2017)CrossRefGoogle Scholar
  21. 21.
    Abdelhafiz, D., Yang, C., Ammar, R., Nabavi, S.: Deep convolutional neural networks for mammography: advances, challenges and applications. BMC Bioinform. 20, 281 (2019)CrossRefGoogle Scholar
  22. 22.
    Mishkin, D., Sergievskiy, N., Matas, J.: Systematic evaluation of convolution neural network advances on the imagenet. Comput. Vis. Image Underst. 161, 11–19 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Wajeeha Ansar
    • 1
  • Ahmad Raza Shahid
    • 1
  • Basit Raza
    • 1
    Email author
  • Amir Hanif Dar
    • 1
  1. 1.Medical Imaging and Diagnostic (MID) Lab, National Centre of Artificial Intelligence (NCAI), Department of Computer ScienceCOMSATS University Islamabad (CUI)IslamabadPakistan

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