Skip to main content

Advertisement

SpringerLink
Log in

A novel multistage CAD system for breast cancer diagnosis

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Computer-aided diagnosis (CAD) systems are widely used to diagnose breast cancer using mammography screening. In this research, we proposed a new multistage CAD system based on image decomposition with High-Dimensional Model Representation (HDMR) which is a divide-and-conquer algorithm. We used digital mammograms from Digital Database for Screening Mammography as dataset. We neglected BIRADS classification and used a brand-new clustering based on HDMR constant and breast size. To find the best performance of HDMR-based CAD system, we compared different pre-processing settings such as contrast enhancement with CLAHE and HDMR, feature extraction with HDMR, feature scaling, dimension reduction with Linear Discriminant Analysis. We used several Machine Learning algorithms and measured the performance of proposed system for normal–benign–malign classification, cancer detection, mass detection and found that the proposed system achieves \(66\%\), \(71\%\) and \(87\%\) accuracy, respectively. We were able to achieve \(92\%\) accuracy, \(100\%\) sensitivity and \(91\%\) specificity in specific clusters. These results are comparable with deep learning-based methods although we simplified the pipeline and used brand-new HDMR-based processes.

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
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Availability of data and materials

Dataset is publicly available at DDSM: Digital Database for Screening Mammography website (http://www.eng.usf.edu/cvprg/mammography/database.html).

References

  1. Doi, K.: Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput. Med. Imaging Graph. 31(4–5), 198–211 (2007)

    Article  Google Scholar 

  2. Tang, J., Member, S., Rangayyan, R.M., Xu, J., El Naqa, I.: Computer-aided detection and diagnosis of breast cancer with mammography: recent advances. IEEE Trans. Inf Technol. Biomed. 13(2), 236–251 (2009)

    Article  Google Scholar 

  3. Summers, R.M., Yao, J., Pickhardt, P.J., Franaszek, M., Bitter, I., Brickman, D., Krishna, V., Choi, J.R.: Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population. Gastroenterology 129(6), 1832–1844 (2005)

    Article  Google Scholar 

  4. Shiraishi, J., Abe, H., Engelmann, R., Aoyama, M.: Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists’ performance-initial experience. Radiology 227(2), 469–474 (2003)

    Article  Google Scholar 

  5. Salas-Gonzalez, D., Górriz, J.M., Ramírez, J., López, M., Álvarez, I., Segovia, F., Puntonet, C.G.: Computer aided diagnosis of Alzheimer disease using support vector machines and classification trees. In: Köppen, M., Kasabov, N., Coghill, G. (eds.) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol. 5507. Springer, Berlin (2009)

    Google Scholar 

  6. Müller, A.C., Guido, S.: Introduction to Machine Learning with Python. O’Reilly Media Inc (2017)

    Google Scholar 

  7. Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, W.P.: The Digital Database for Screening Mammography, pp. 212–218. Medical Physics Publishing, New York (2001)

    Google Scholar 

  8. Oliveira, J.E.E., Gueld, M.O., Araujo, A.D.A., Ott, B., Deserno, T.M.: Towards a standard reference database for computer-aided mammography. Proc. SPIE Med. Imaging Comput. Aided Diagn. 6915(2), 1–9 (2008)

    Google Scholar 

  9. Eltoukhy, M., Faye, I., Samir, 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. Sharma, S., Khanna, P.: Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM. J. Digit. Imaging 28(1), 77–90 (2014)

    Article  Google Scholar 

  11. Cordeiro, F.R., Santos, W.P., Silva-Filho, A.G.: A semi-supervised fuzzy GrowCut algorithm to segment and classify regions of interest of mammographic images. Expert Syst. Appl. 65, 116–126 (2016)

    Article  Google Scholar 

  12. De Lima, S.M.L., da Silva-Filho, A.G., dos Santos, W.P.: Detection and classification of masses in mammographic images in a multi-kernel approach. Comput. Methods Programs Biomed. 134, 11–29 (2016)

    Article  Google Scholar 

  13. Kamra, A., Jain, V.K., Singh, S., Mittal, S.: Characterization of architectural distortion in mammograms based on texture analysis using support vector machine classifier with clinical evaluation. J. Digit. Imaging 29(1), 104–114 (2015)

    Article  Google Scholar 

  14. Shastri, A.A., Tamrakar, D., Ahuja, K.: Density-wise two stage mammogram classification using texture exploiting descriptors. Expert Syst. Appl. 99, 71–82 (2018)

    Article  Google Scholar 

  15. Alolfe, M.A., Youssef, A.-B.M., Kadah, Y.M., Mohamed, A.S.: Development of a computer-aided classification system for cancer detection from digital mammograms. In: 2008 National Radio Science Conference. https://doi.org/10.1109/NRSC.2008.4542383 (2008)

  16. Sobol, I.M.: Sensitivity estimates for nonlinear mathematical models. Math. Modell. Comput. Exp. 1, 407–414 (1993)

    MathSciNet  MATH  Google Scholar 

  17. Tunga, M.A., Demiralp, M.: A new approach for data partitioning through high dimensional model representation. Int. J. Comput. Math. 85(12), 1779–1792 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  18. Tunga, B., Demiralp, M.: A novel approximation method for multivariate data partitioning fluctuation free integration based HDMR. Eng. Comput. 29(7–8), 743–765 (2012)

    Article  Google Scholar 

  19. Özay, E.K., Demiralp, M.: Reductive enhanced multivariance product representation for multi-way arrays. J. Math. Chem. 52(10), 2546–2558 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  20. Korkmaz, E., Demiralp, M.: Combined small scale high dimensional model representation. J. Math. Chem. 50, 2023–2042 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  21. Tunga, M.A., Demiralp, M.: Computational complexity investigations for high-dimensional model representation algorithms used in multivariate interpolation problems. In: Advances in Numerical Methods, pp. 15–29. Springer, New York (2009)

  22. Tunga, B., Demiralp M.: An iterative enhanced product representation scheme for multivariate function decomposition. In: International Conference on Applied Computer Science, Malta (2010)

  23. Tunga, M.A., Demiralp, M.: A factorized high dimensional model representation on the nodes of a finite hyperprismatic regular grid. Appl. Math. Comput. 164(3), 865–883 (2005)

    MathSciNet  MATH  Google Scholar 

  24. Baran, M., Bieniasz, L.K.: Experiments with an adaptive multicut-HDMR map generation for slowly varying continuous multivariate functions. Appl. Math. Comput. 258, 206–219 (2005)

    MathSciNet  MATH  Google Scholar 

  25. Altın, E.M., Tunga, B.: High dimensional model representation in image processing. In: Proceedings of the 2014 International Conference on Computational and Mathematical Methods in Science and Engineering, Cadiz, Spain (2014)

  26. Karcılı, A., Tunga, B.: High dimensional model representation (HDMR) with clustering for image retrieval. In: ICNPAA World Congress: International Conference in Nonlinear Problems in Aviation and Aerospace. La Rochelle, France (2016)

  27. Tunga, B., Koçanaoğulları, A.: Digital image decomposition and contrast enhancement using high-dimensional model representation. SIViP 12(2), 299–306 (2017)

    Article  Google Scholar 

  28. Karaca, E., Tunga, M.A.: An interpolation-based texture and pattern preserving algorithm for inpainting color images. Expert Syst. Appl. 91, 223–234 (2018)

    Article  Google Scholar 

  29. Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, W.P., Moore, R., Chang, K., MunishKumaran, S.: Current status of the digital database for screening mammography. In: Digital Mammography, pp. 457–460 (1998)

  30. Bassett, L.W., Hendrick, R.E.: Quality determinants of mammography: clinical practice guideline. In: Agency for Health Care Policy and Research Publication (Chapter 3) (1994)

  31. Baert, A.L., Brady, L.W., Heilmann, H.P., Hricak, H., Knauth, M., Molls, M., Nieder, C., Reiser, M.F.: Digital mammography. In: Bick, U., Diekmann, F. (eds.) Medical Radiology Diagnostic Imaging and Radiation Oncology, pp. 69–74. Springer (2010)

    Google Scholar 

  32. Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., Romeny, B.T.H., Zimmerman, J.B., Zuiderveld, K.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39, 355–368 (1987)

    Article  Google Scholar 

  33. Sundaram, M., Ramar, K., Arumugam, N., Prabin, G.: Histogram based contrast enhancement for mammogram images. In: International Conference on Signal Processing, Communication, Computing and Networking Technologies (2011). https://doi.org/10.1109/icsccn.2011.6024667

  34. Rangayyan, R.M.: Enhancement. In: Bankmann, I.H. (ed.) Handbook of Medical Image Processing and Analysis, pp. 1–2. Elsevier, New York (2009)

    Google Scholar 

  35. James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning: With Applications in R. Springer, New York (2013)

    Book  MATH  Google Scholar 

  36. Verma, B., McLeod, P., Klevansky, A.: Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer. Expert Syst. Appl. 37(4), 3344–3351 (2010)

    Article  Google Scholar 

  37. 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 

  38. Kumar, I.H.S.B., Virmani, J., Thakur, S.: A classification framework for prediction of breast density using an ensemble of neural network classifiers. Biocybern. Biomed. Eng. 37(1), 217–228 (2017)

  39. Hussain, Z., Gimenez, F., Yi, D., Rubin, D.: Differential data augmentation techniques for medical imaging classification tasks. In: AMIA Annual Symposium Proceedings. AMIA Symposium, 2017, pp. 979–984 (2018)

  40. Guan, S., Loew, M.: Breast cancer detection using synthetic mammograms from generative adversarial networks in convolutional neural networks. J. Med. Imaging (Bellingham, Washington) 6(3), 031411 (2019)

  41. Shaikh, K., Krishnan, S., Thanki, R.: Artificial Intelligence in Breast Cancer Early Detection and Diagnosis. Springer, Cham (2021)

    Book  Google Scholar 

Download references

Funding

This research did not receive any specific grant.

Author information

Author notes

  1. Tevfik Uyar, Burcu Tunga, M. Alper Tunga have contributed equally to this work.

Authors and Affiliations

  1. Software Engineering Department, Faculty of Engineering and Natural Sciences, Bahçeşehir University, 34349, Beşiktaş, Istanbul, Turkey

    Kübra Karacan

  2. Faculty of Economics and Administrative Sciences, İstanbul Kültür University, Basın Express Campus, 34303, İstanbul, Turkey

    Tevfik Uyar

  3. Mathematics Engineering Department, Faculty of Arts and Sciences, İstanbul Technical University, 34469, Maslak, İstanbul, Turkey

    Kübra Karacan & Burcu Tunga

  4. Computer Engineering Department, Faculty of Engineering and Natural Sciences, İstinye University, 34396, Sarıyer, İstanbul, Turkey

    M. Alper Tunga

Authors
  1. Kübra Karacan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tevfik Uyar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Burcu Tunga
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Alper Tunga
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors have contributed equally.

Corresponding author

Correspondence to Kübra Karacan.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest.

Ethical approval

This research is built on DDSM data, which is publicly available and not linked to individuals. Therefore, there shall be no ethical issues.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karacan, K., Uyar, T., Tunga, B. et al. A novel multistage CAD system for breast cancer diagnosis. SIViP 17, 2359–2368 (2023). https://doi.org/10.1007/s11760-022-02453-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-022-02453-3

Keywords

Search

Navigation