Skip to main content
SpringerLink
Log in

A Novel Approach for False Positive Reduction in Breast Cancer Detection

  • Conference paper
  • First Online:
Computer Vision and Image Processing (CVIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1148))

Included in the following conference series:

  • 723 Accesses

Abstract

Breast Cancer is the most prevalent cancer among women across the globe. Objective of the Computer Aided Diagnosis (CAD) in breast cancer analysis is to detect the tumorous mass such that patient could get the proper treatment within time. However, existing algorithm undergoes detection of false positives. Thus, reduction of false positives is one of the challenging tasks to improve the performance of the diagnosis systems. In this paper, we propose a convolution neural network based approach for false positive reduction. We propose residual learning (ResNet) for false positive reduction. Masses segmented using a respective segmentation algorithm are given as an input to the proposed network to classify between true positive (tumorous mass) and false positive (non-tumorous mass). Proposed approach is validated on set of mammography scans collected from the Tata Memorial Cancer Hospital (TMCH). The performance of proposed algorithm is measured using precision, recall and F-score and compared with existing deep networks. Performance analysis shows that proposed approach outperforms other existing deep networks for false positives reduction.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abdel-Nasser, M., Rashwan, H.A., Puig, D., Moreno, A.: Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern. Expert Syst. Appl. 42(24), 9499–9511 (2015)

    Article  Google Scholar 

  2. Anand, S., Gayathri, S.: Mammogram image enhancement by two-stage adaptive histogram equalization. Optik 126(21), 3150–3152 (2015)

    Article  Google Scholar 

  3. Berber, T., Alpkocak, A., Balci, P., Dicle, O.: Breast mass contour segmentation algorithm in digital mammograms. Comput. Methods Programs Biomed. 110(2), 150–159 (2013)

    Article  Google Scholar 

  4. Demirhan, A., Güler, İ.: Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation. Eng. Appl. Artif. Intell. 24(2), 358–367 (2011)

    Article  Google Scholar 

  5. Dudhane, A., Shingadkar, G., Sanghavi, P., Jankharia, B., Talbar, S.: Interstitial lung disease classification using feed forward neural networks. In: International Conference on Communication and Signal Processing 2016 (ICCASP 2016). Atlantis Press (2016)

    Google Scholar 

  6. Dudhane, A., Murala, S.: \(\text{C}{\hat{}}\) 2MSNet: a novel approach for single image haze removal. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1397–1404. IEEE (2018)

    Google Scholar 

  7. Dudhane, A., Singh Aulakh, H., Murala, S.: RI-GAN: an end-to-end network for single image haze removal. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)

    Google Scholar 

  8. Dudhane, A.A., Talbar, S.N.: Multi-scale directional mask pattern for medical image classification and retrieval. In: Chaudhuri, B.B., Kankanhalli, M.S., Raman, B. (eds.) Proceedings of 2nd International Conference on Computer Vision & Image Processing. AISC, vol. 703, pp. 345–357. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7895-8_27

    Chapter  Google Scholar 

  9. Eltoukhy, M.M., Faye, I., Samir, B.B.: A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram. Comput. Biol. Med. 40(4), 384–391 (2010)

    Article  Google Scholar 

  10. Eltoukhy, M.M., Faye, I., Samir, B.B.: A statistical based feature extraction method for breast cancer diagnosis in digital mammogram using multiresolution representation. Comput. Biol. Med. 42(1), 123–128 (2012)

    Article  Google Scholar 

  11. Galshetwar, G.M., Patil, P.W., Gonde, A.B., Waghmare, L.M., Maheshwari, R.: Local directional gradient based feature learning for image retrieval. In: IEEE 13th International Conference on Industrial and Information Systems (ICIIS), pp. 113–118. IEEE (2018)

    Google Scholar 

  12. Gandhamal, A., Talbar, S., Gajre, S., Hani, A.F.M., Kumar, D.: Local gray level S-curve transformation-a generalized contrast enhancement technique for medical images. Comput. Biol. Med. 83, 120–133 (2017)

    Article  Google Scholar 

  13. Ganesan, K., Acharya, U.R., Chua, K.C., Min, L.C., Abraham, K.T.: Pectoral muscle segmentation: a review. Comput. Methods Programs Biomed. 110(1), 48–57 (2013)

    Article  Google Scholar 

  14. Ghadage, S., Pawar, M.: Integration of local features for brain tumour segmentation. In: IEEE 13th International Conference on Industrial and Information Systems (ICIIS), pp. 173–178. IEEE (2018)

    Google Scholar 

  15. Gonde, A.B., Patil, P.W., Galshetwar, G.M., Waghmare, L.M.: Volumetric local directional triplet patterns for biomedical image retrieval. In: Fourth International Conference on Image Information Processing (ICIIP), pp. 1–6. IEEE (2017)

    Google Scholar 

  16. Gupta, A., Shridhar, K., Dhillon, P.: A review of breast cancer awareness among women in India: cancer literate or awareness deficit? Eur. J. Cancer 51(14), 2058–2066 (2015)

    Article  Google Scholar 

  17. Hambarde, P., Talbar, S.N., Sable, N., Mahajan, A., Chavan, S.S., Thakur, M.: Radiomics for peripheral zone and intra-prostatic urethra segmentation in MR imaging. Biomed. Signal Process. Control 51, 19–29 (2019)

    Article  Google Scholar 

  18. 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 

  19. Hussain, M.: False-positive reduction in mammography using multiscale spatial Weber law descriptor and support vector machines. Neural Comput. Appl. 25(1), 83–93 (2013). https://doi.org/10.1007/s00521-013-1450-7

    Article  Google Scholar 

  20. Kanadam, K.P., Chereddy, S.R.: Mammogram classification using sparse-ROI: a novel representation to arbitrary shaped masses. Expert Syst. Appl. 57, 204–213 (2016)

    Article  Google Scholar 

  21. Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)

    Article  Google Scholar 

  22. 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 

  23. Li, Y., Chen, H., Yang, Y., Cheng, L., Cao, L.: A bilateral analysis scheme for false positive reduction in mammogram mass detection. Comput. Biol. Med. 57, 84–95 (2015)

    Article  Google Scholar 

  24. Liu, X., Zeng, Z.: A new automatic mass detection method for breast cancer with false positive reduction. Neurocomputing 152, 388–402 (2015)

    Article  Google Scholar 

  25. Lladó, X., Oliver, A., Freixenet, J., Martí, R., Martí, J.: A textural approach for mass false positive reduction in mammography. Comput. Med. Imaging Graph. 33(6), 415–422 (2009)

    Article  Google Scholar 

  26. Maitra, I.K., Nag, S., Bandyopadhyay, S.K.: Technique for preprocessing of digital mammogram. Comput. Methods Programs Biomed. 107(2), 175–188 (2012)

    Article  Google Scholar 

  27. Malvia, S., Bagadi, S.A., Dubey, U.S., Saxena, S.: Epidemiology of breast cancer in Indian women. Asia Pac. J. Clin. Oncol. 13(4), 289–295 (2017)

    Article  Google Scholar 

  28. Muramatsu, C., Hara, T., Endo, T., Fujita, H.: Breast mass classification on mammograms using radial local ternary patterns. Comput. Biol. Med. 72, 43–53 (2016)

    Article  Google Scholar 

  29. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29(1), 51–59 (1996)

    Article  Google Scholar 

  30. Oliver, A., et al.: A review of automatic mass detection and segmentation in mammographic images. Med. Image Anal. 14(2), 87–110 (2010)

    Article  Google Scholar 

  31. Patil, P., Murala, S., Dhall, A., Chaudhary, S.: MsEDNet: multi-scale deep saliency learning for moving object detection. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1670–1675. IEEE (2018)

    Google Scholar 

  32. Patil, P.W., Murala, S.: MSFgNet: a novel compact end-to-end deep network for moving object detection. IEEE Trans. Intell. Trans. Syst. 20(11), 4066–4077 (2018)

    Article  Google Scholar 

  33. Pawar, M.M., Talbar, S.N.: Genetic fuzzy system (GFS) based wavelet co-occurrence feature selection in mammogram classification for breast cancer diagnosis. Perspect. Sci. 8, 247–250 (2016)

    Article  Google Scholar 

  34. Pawar, M.M., Talbar, S.N.: Local entropy maximization based image fusion for contrast enhancement of mammogram. J. King Saud Univ. Comput. Inf. Sci. (2018)

    Google Scholar 

  35. Pawar, M.M., Talbar, S.N., Dudhane, A.: Local binary patterns descriptor based on sparse curvelet coefficients for false-positive reduction in mammograms. J. Healthc. Eng.2018 (2018)

    Google Scholar 

  36. Pawar, M.M., Talbar, S.N., Dudhane, A.: Tata Memorial Cancer Hospital database (2018). http://eureka.sveri.ac.in/blog/2018/06/17/tmch-database/. Accessed 15 Aug 2019

  37. Raghavendra, U., Acharya, U.R., Fujita, H., Gudigar, A., Tan, J.H., Chokkadi, S.: Application of Gabor wavelet and locality sensitive discriminant analysis for automated identification of breast cancer using digitized mammogram images. Appl. Soft Comput. 46, 151–161 (2016)

    Article  Google Scholar 

  38. Rouhi, R., Jafari, M.: Classification of benign and malignant breast tumors based on hybrid level set segmentation. Expert Syst. Appl. 46, 45–59 (2016)

    Article  Google Scholar 

  39. Rouhi, R., Jafari, M., Kasaei, S., Keshavarzian, P.: Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst. Appl. 42(3), 990–1002 (2015)

    Article  Google Scholar 

  40. Salazar-Licea, L.A., Pedraza-Ortega, J.C., Pastrana-Palma, A., Aceves-Fernandez, M.A.: Location of mammograms ROI’s and reduction of false-positive. Comput. Methods Programs Biomed. 143, 97–111 (2017)

    Article  Google Scholar 

  41. Shaha, M., Pawar, M.: Transfer learning for image classification. In: Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 656–660. IEEE (2018)

    Google Scholar 

  42. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  43. Thengane, V.G., Gawande, M.B., Dudhane, A.A., Gonde, A.B.: Cycle face aging generative adversarial networks. In: IEEE 13th International Conference on Industrial and Information Systems (ICIIS), pp. 125–129. IEEE (2018)

    Google Scholar 

Download references

Acknowledgement

The TMCH database used for this work was given by Department of Radiodiagnosis, Tata Memorial Cancer Hospital, Mumbai.

Author information

Authors and Affiliations

  1. SVERI, Pandharpur, Maharashtra, India

    Mayuresh Shingan & Meenakshi Pawar

  2. SGGS IE&T, Nanded, Maharashtra, India

    S. Talbar

Authors
  1. Mayuresh Shingan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Meenakshi Pawar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Talbar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mayuresh Shingan .

Editor information

Editors and Affiliations

  1. Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Neeta Nain

  2. Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Santosh Kumar Vipparthi

  3. Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shingan, M., Pawar, M., Talbar, S. (2020). A Novel Approach for False Positive Reduction in Breast Cancer Detection. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1148. Springer, Singapore. https://doi.org/10.1007/978-981-15-4018-9_33

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-4018-9_33

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4017-2

  • Online ISBN: 978-981-15-4018-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation