Skip to main content
SpringerLink
Log in

Classification of Breast Abnormalities Using a Deep Convolutional Neural Network and Transfer Learning

  • THEORY AND METHODS OF INFORMATION PROCESSING
  • Published:
Journal of Communications Technology and Electronics Aims and scope Submit manuscript

Abstract

A new algorithm for classification of breast pathologies in digital mammography using a convolutional neural network and transfer learning is proposed. The following pretrained neural networks were chosen: MobileNetV2, InceptionResNetV2, Xception, and ResNetV2. All mammographic images were pre-processed to improve classification reliability. Transfer training was carried out using additional data augmentation and fine-tuning. The performance of the proposed algorithm for classification of breast pathologies in terms of accuracy on real data is discussed and compared with that of state-of-the-art algorithms on the available MIAS database.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.

Similar content being viewed by others

REFERENCES

  1. F. Bray, J. Ferlay, I. Soerjomataram, et al., “Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: A Cancer J. Clinicians 68, 394–424 (2018).

  2. J. Diaz-Escobar and V. Kober, “Classification of breast abnormalities in digital mammography using phase-based features,” Proc. SPIE 11137, 1113724 (2019).

    Google Scholar 

  3. J. Diaz-Escobar and V. Kober, “V. K. M. M. Recognition of breast abnormalities using phase features,” J. Commun. Technol. Electron. 65, 1476–1483 (2020).

  4. M. A. Al-antari, M. A. Al-masni, M.-T. Choi, et al., “A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification,” Int. J. Med. Inf. 117, 44–54 (2018).

  5. R. W. D. Pedro, A. Machado-Lima, and F. L. Nunes, “Is mass classification in mammograms a solved problem?—a critical review over the last 20 years,” Expert Syst. Appl. 119, 90–103 (2019).

  6. A. D. Trister and D. L. C. I. Buist, “Will machine learning tip the balance in breast cancer screening?,” JAMA Oncol. 3, 1463–1464 (2017).

  7. L. Mina and N. A. M. Isa, “A review of computer-aided detection and diagnosis of breast cancer in digital mammography,” J. Med. Sci. 15, 110–121 (2015).

  8. J. Bozek, M. Mustra, K. Delac, and M. Grgic, “A survey of image processing algorithms in digital mammography,” in Recent Advances in Multimedia Signal Processing and Communications, Ed. by M. Grgic, K. Delac, and M. Ghanbari (Springer-Verlag, Berlin, 2009), pp. 631–657.

  9. M. Salama, A. Eltrass, and H. Elkamchouchi, “An improved approach for computer-aided diagnosis of breast cancer in digital mammography,” in IEEE Int. Symp. on Medical Measurements and Applications (MeMeA), June, 2018 (IEEE, New York, 2018), pp. 1–5.

  10. K. Doi, “Computer-aided diagnosis in medical imaging: Historical review, current status and future potential,” Comput. Med. Imaging Graph.: Official J. Comput. Medi. Imaging Soc. 31 (06), 198–211 (2007).

  11. B. Halalli and A. Makandar, “Computer aided diagnosis—medical image analysis techniques,” in Breast Imaging, Ed. by Cherie M. Kuzmiak (IntechOpen, Rijeka, 2018).

    Google Scholar 

  12. R. Murakami, “Detection of breast cancer with a computer-aided detection applied to full-field digital mammography,” J. Digital Imaging 26, 768–773 (2013).

    Article  Google Scholar 

  13. S. Boughorbel, R. Al-Ali, and N. Elkum, “Model comparison for breast cancer prognosis based on clinical data,” PloS one 11, e0146413 (2016).

    Article  Google Scholar 

  14. A. Shrivastava, A. Chaudhary, D. Kulshreshtha, et al., “Automated digital mammogram segmentation using dispersed region growing and sliding window algorithm,” in Proc. 2nd Int. Conf. on Image, Vision and Computing (ICIVC), 2017 (ICIVC, 2017), pp. 366–370 (2017).

  15. L. Morra, “Breast cancer: Computer-aided detection with digital breast tomosynthesis,” Radiology 277, 56–63 (2015).

  16. S. Leonov, A. Vasilyev, A. Makovetskii, and A. Kober, “Analysis of the convolutional neural network architectures in image classification problems,” Proc. SPIE 11137, 111372E (2019).

    Google Scholar 

  17. L. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT Press, 2016).

    MATH  Google Scholar 

  18. M. M. Abdelsamea, M. H. Mohamed, and M. Bamatraf, “Automated classification of malignant and benign breast cancer lesions using neural networks on digitized mammograms,” Cancer Inf. 18, 1176935119857570 (2019).

    Google Scholar 

  19. L. G. Falconi, M. Perez, and W. G. Aguilar, “Transfer learning in breast mammogram abnormalities classification with mobilenet and nasnet,” in Proc. 2019 Int. Conf. on Systems, Signals and Image Processing (IWSSIP), 2019 (IWSSIP, 2019), pp. 109–114.

  20. H. Mohamed, M. S. Mabrouk, and A. Sharawy, “Computer aided detection system for micro calcifications in digital mammograms,” Comput. Methods & Programs Biomed. 116, 226–235 (2014).

  21. A. Charate and S. Jamge, “The preprocessing methods of mammogram images for breast cancer detection,” Int. J. on Recent & Innovation Trends in Comput. and Commun. 5, 261–264 (2017).

  22. F. Eddaoudi and F. Regragui, “Microcalcifications detection in mammographic images using texture coding,” Appl. Math. Sci. 5 (01), 381–393 (2011).

  23. S. Charan, M. J. Khan, and K. Khurshid, “Breast cancer detection in mammograms using convolutional neural network,” in Proc. 2018 Int. Conf. on Computing, Mathematics and Engineering Technologies (iCoMET), 2018 (iCoMET, 2018), pp. 1–5 (2018).

  24. V. Kober, “Robust and efficient algorithm of image enhancement,” IEEE Trans. Consum. Electron. 52, 655– 659 (2006).

  25. V. Kober, “Fast recursive computation of sliding dht with arbitrary step,” Sensors 20, 5556 (2020).

    Article  Google Scholar 

  26. V. K. Singh, H. A. Rashwan, S. Romani, et al., “Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network,” Expert Syst. Appl. 139, 112855 (2020).

    Article  Google Scholar 

  27. P. Christ, F. Ettlinger, F. Grün, et al., “Automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks,” Sci. Reports 8, 15497 (2018).

    Google Scholar 

  28. N. J. Tustison, B. B. Avants, P. A. Cook, et al., “N4itk: Improved n3 bias correction,” IEEE Trans. Med. Imaging 29, 1310–1320 (2010).

    Article  Google Scholar 

  29. K. Kamnitsas, C. Ledig, V. F. Newcombe, et al., “Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,” Med. Image Anal. 36, 61–78 (2017).

  30. Z. Zhou, J. Shin, L. Zhang, et al., “Fine-tuning convolutional neural networks for biomedical image analysis: Actively and incrementally,” in Proc. 2017 IEEE Conf. on Comput. & Vision Pattern Rec. (CVPR) 2017 (IEEE, New York, 2017), pp. 4761–4772.

  31. J. Zhang, Y. Xie, Q. Wu, and Y. Xia, “Medical image classification using synergic deep learning,” Med. Image Anal. 54, 10–19 (2019).

  32. http://peipa.essex.ac.uk/info/mias.html.

  33. A. Ruchay and V. Kober, “Impulsive noise removal from color images with morphological filtering,” Analysis Images, Social Networks and Texts (Springer), 280–291 (2018).

  34. A. Ruchay, A. Kober, V. Kolpakov, and T. Makovetskaya, “Removal of impulsive noise from color images with cascade switching algorithm,” Proc. SPIE 10752, 1075224-12 (2018).

  35. A. Ruchay, K. Dorofeev, and V. Kalschikov, “A novel switching bilateral filtering algorithm for depth map,” Comput. Opt. 43, 1001–1007 (2019).

  36. A. C. Perre, L. A. Alexandre, and L. C. Freire, “Lesion classification in mammograms using convolutional neural networks and transfer learning,” Comput. Methods in Biomech. Biomed. Eng.: Imaging & Visual. 7, 550–556 (2019).

Download references

Author information

Authors and Affiliations

  1. Chelyabinsk State University, 454001, Chelyabinsk, Russia

    A. N. Ruchai, V. I. Kober & K. A. Dorofeev

  2. Institute for Information Transmission Problems (Kharkevich Institute), 127051, Moscow, Russia

    V. I. Kober, V. N. Karnaukhov & M. G. Mozerov

  3. South Ural State University, 454080, Chelyabinsk, Russia

    A. N. Ruchai

  4. Center of Scientific Research and Higher Education, B.C 22860, Ensenada, Mexico

    V. I. Kober

Authors
  1. A. N. Ruchai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. I. Kober
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. A. Dorofeev
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. V. N. Karnaukhov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. G. Mozerov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to A. N. Ruchai, V. I. Kober, K. A. Dorofeev, V. N. Karnaukhov or M. G. Mozerov.

Additional information

Translated by F. Baron

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ruchai, A.N., Kober, V.I., Dorofeev, K.A. et al. Classification of Breast Abnormalities Using a Deep Convolutional Neural Network and Transfer Learning. J. Commun. Technol. Electron. 66, 778–783 (2021). https://doi.org/10.1134/S1064226921060206

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1064226921060206

Search

Navigation