Advertisement

Deep Learning Computer-Aided Diagnosis for Breast Lesion in Digital Mammogram

  • Mugahed A. Al-antari
  • Mohammed A. Al-masni
  • Tae-Seong KimEmail author
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1213)

Abstract

For computer-aided diagnosis (CAD), detection, segmentation, and classification from medical imagery are three key components to efficiently assist physicians for accurate diagnosis. In this chapter, a completely integrated CAD system based on deep learning is presented to diagnose breast lesions from digital X-ray mammograms involving detection, segmentation, and classification. To automatically detect breast lesions from mammograms, a regional deep learning approach called You-Only-Look-Once (YOLO) is used. To segment breast lesions, full resolution convolutional network (FrCN), a novel segmentation model of deep network, is implemented and used. Finally, three conventional deep learning models including regular feedforward CNN, ResNet-50, and InceptionResNet-V2 are separately adopted and used to classify or recognize the detected and segmented breast lesion as either benign or malignant. To evaluate the integrated CAD system for detection, segmentation, and classification, the publicly available and annotated INbreast database is used over fivefold cross-validation tests. The evaluation results of the YOLO-based detection achieved detection accuracy of 97.27%, Matthews’s correlation coefficient (MCC) of 93.93%, and F1-score of 98.02%. Moreover, the results of the breast lesion segmentation via FrCN achieved an overall accuracy of 92.97%, MCC of 85.93%, Dice (F1-score) of 92.69%, and Jaccard similarity coefficient of 86.37%. The detected and segmented breast lesions are classified via CNN, ResNet-50, and InceptionResNet-V2 achieving an average overall accuracies of 88.74%, 92.56%, and 95.32%, respectively. The performance evaluation results through all stages of detection, segmentation, and classification show that the integrated CAD system outperforms the latest conventional deep learning methodologies. We conclude that our CAD system could be used to assist radiologists over all stages of detection, segmentation, and classification for diagnosis of breast lesions.

Keywords

Medical image analysis Mammograms Breast lesion Computer-aided diagnosis (CAD) Deep learning Full resolution convolutional network (FrCN) Detection Segmentation Classification 

Notes

Acknowledgements

This work was supported by International Collaborative Research and Development Programme funded by the Ministry of Trade, Industry and Energy (MOTIE, Korea) (N0002252). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (2019R1A2C1003713).

References

  1. 1.
    Siegel RL, Miller KD, Jemal A (2017) Cancer statistics, 2017. CA Cancer J Clin 67(1):7–30CrossRefGoogle Scholar
  2. 2.
    Al-antari MA, Al-masni MA, Park SU, Park JH, Kadah YM, Han SM, Kim T-S (2016) Automatic computer-aided diagnosis of breast cancer in digital mammograms via deep belief network. In: Global conference on engineering and applied science (GCEAS), Japan, pp 1306–1314Google Scholar
  3. 3.
    Jill J (2015) Breast cancer screening guidelines in the United States. JAMA 314:1658–1658CrossRefGoogle Scholar
  4. 4.
    Al-antari MA, Al-masni MA, Park SU, Park JH, Metwally MK, Kadah YM, Han SM, Kim T-S (2017) An automatic computer-aided diagnosis system for breast cancer in digital mammograms via deep belief network. J Med Biol Eng.  https://doi.org/10.1007/s40846-017-0321-6 CrossRefGoogle Scholar
  5. 5.
    Al-antaria MA, Al-masni MA, Choi M-T, Han S-M (2018) A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int J Med Inform 117:44–54CrossRefGoogle Scholar
  6. 6.
    Casellas-Grau A, Vives J, Font A, Ochoa C (2016) Positive psychological functioning in breast cancer: an integrative review. Breast 27:136–168CrossRefGoogle Scholar
  7. 7.
    Al-Masni MA, Al-Antari MA, Park JM, Gi G, Kim TY, Rivera P, Valarezo E, Choi MT, Han SM, Kim TS (2018) Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput Methods Programs Biomed 157:85–94CrossRefGoogle Scholar
  8. 8.
    Dhungel N, Carneiro G, Bradley AP (2017) A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med Image Anal 37(1):114–128CrossRefGoogle Scholar
  9. 9.
    Yassin NI, Omran S, Houby EM, Allam H (2018) Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: a systematic review. Comput Methods Programs Biomed 156:25–45CrossRefGoogle Scholar
  10. 10.
    Chakraborty J, Midya A, Rabidas R (2018) Computer-aided detection and diagnosis of mammographic masses using multi-resolution analysis of oriented tissue patterns. Exp Syst Appl 99(1):168–179CrossRefGoogle Scholar
  11. 11.
    Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: 2016 IEEE conference on computer vision and pattern recognition, pp 779–788Google Scholar
  12. 12.
    Moreira I, Amaral I, Domingues I, Cardoso A, Cardoso M, Cardoso J (2012) INbreast: toward a full-field digital mammographic database. Acad Radiol 19(2):236–248CrossRefGoogle Scholar
  13. 13.
    Jiao Z, Gao X, Wang Y, Li J (2016) A deep feature based framework for breast masses classification. Neurocomputing 197(C):221–231CrossRefGoogle Scholar
  14. 14.
    Carneiro G, Nascimento J, Bradley AP (2017) Automated analysis of unregistered multi-view mammograms with deep learning. IEEE Trans Med Imaging 36(11):2355–2365CrossRefGoogle Scholar
  15. 15.
    Al-masni MA, Al-antari MA, Park JM, Gi G, Kim TY, Rivera P, Valarezo E, Han S-M, Kim T-S (2017) Detection and classification of the breast abnormalities in digital mammograms via regional Convolutional Neural Network. In: 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC’17), Jeju Island, South Korea, 2017, pp 1230–1236Google Scholar
  16. 16.
    Ayelet A-B, Karlinsky L, Alpert S, Hasoul S, Ben-Ari R, Barkan E (2016) A region based convolutional network for tumor detection and classification in breast mammography. In: International workshop on large-scale annotation of biomedical data and expert label synthesis. Springer, Athens, pp 197–205Google Scholar
  17. 17.
    Cardoso JS, Domingues I, Oliveira HP (2015) Closed shortest path in the original coordinates with an application to breast cancer. Int J Pattern Recognit Artif Intell 29(1):2CrossRefGoogle Scholar
  18. 18.
    Yuan Y, Chao M, Lo Y-C (2017) Automatic skin lesion segmentation using deep fully convolutional networks with Jaccard distance. IEEE Trans Med Imaging 36(9):1876–1886CrossRefGoogle Scholar
  19. 19.
    Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted interventionGoogle Scholar
  20. 20.
    Badrinarayanan V, Kendall A, Cipoll R (2016) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561Google Scholar
  21. 21.
    Kooi T, Litjens G, van Ginneken B, Gubern-Mérida A, Sánchez CI, Mann R, den Heeten A, Karssemeijer N (2017) Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 35:303–312CrossRefGoogle Scholar
  22. 22.
    Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: 25th international conference on neural information processing systems, USA, 2012, pp 1097–1105Google Scholar
  23. 23.
    Shelhamer E, Long J, Darrell T (2017) Fully Convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651CrossRefGoogle Scholar
  24. 24.
    He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. arXiv:1602.07261v2, pp 770–787Google Scholar
  25. 25.
    Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprintarXiv:1409.1556Google Scholar
  26. 26.
    Szegedy V, Ioffe S, Vanhoucke V (2016) Inception-v4, Inception-ResNet and the impact of residual connections on learning. arXiv:1602.07261v2 [cs.CV]Google Scholar
  27. 27.
    Lab L (2017) Theano. University of Montreal [Online]. Available: http://deeplearning.net/software/theano/tutorial/. Accessed 10 2017
  28. 28.
    Chollet F (2017) Keras: the Python deep learning library. MIT, [Online]. Available: https://keras.io/. Accessed 10 2017
  29. 29.
    Google Brain Team, TensorFlow, 9 11 2017. [Online]. Available: www.tensorflow.org. Accessed 10 2017
  30. 30.
    Kozegar E, Soryani M, Minaei B, Inês D (2013) Assessment of a novel mass detection algorithm in mammograms. J Cancer Res Ther:592–600CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mugahed A. Al-antari
    • 1
    • 2
  • Mohammed A. Al-masni
    • 1
  • Tae-Seong Kim
    • 1
    Email author
  1. 1.Department of Biomedical Engineering, College of Electronics and InformationKyung Hee UniversityYonginRepublic of Korea
  2. 2.Department of Biomedical EngineeringSana’a Community CollegeSana’aRepublic of Yemen

Personalised recommendations