Skip to main content

Advertisement

Log in

Breast cancer detection based on Gabor-wavelet transform and machine learning methods

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Among the major causes of female mortality, breast cancer used to pose big challenges to the medical world. Currently, the most popular method of monitoring and diagnoses—in addition to mammography—is carrying out repeated biopsies to locate the tumor further, that may result in loss of breast tissues. This paper presents an effective method of classifying and detecting the masses in mammograms. In the proposed method, we first attain the feature vector pertaining to each mammography image based on Gabor wavelet transform. Then, we performed tenfold cross validation through several experiments, analyzing the data complexity on each fold. We also used some machine learning methods as decision-making stage and achieved mean accuracies above 0.939, mean sensitivities as high as 0.951, and the mean specificities greater than 0.92. Evaluations and comparisons witness the effectiveness of the proposed method for better diagnosis of breast cancer against the known classification techniques developed in mammography. Simplicity, robustness and high accuracy are advantages of the proposed method.

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

Access this article

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
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Elsayad AM, Elsalamony HA (2013) Diagnosis of breast cancer using decision tree models and SVM 0975-8887. Int J Comput Appl 83(5):19–29

    Google Scholar 

  2. Raghavendra U et al (2016) Application of Gabor wavelet and locality sensitive discriminant analysis for automated identification of breast cancer using digitized mammogram images. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2016.04.036

    Google Scholar 

  3. Desantis C, Siegel R, Jemal A (2015) Breast cancer facts and figs. 2015–2016. Am Cancer Soc. https://doi.org/10.1016/B978-1-4377-1757-0.00028-7

    Google Scholar 

  4. Karabatak M, Cevdet M (2009) An expert system for detection of breast cancer based on association rules and neural network. Expert Syst Appl 36:3465–3469

    Article  Google Scholar 

  5. Singh SM, Rajkumar R, Hemachandran K (2013) Comparative study on content based image retrieval based on Gabor texture features at different scales of frequency and orientations. Int J Comput Appl 78(7):1–7

    Google Scholar 

  6. Kovalerchuc B, Triantaphyllou E, Ruiz JF, Clayton J (1997) Fuzzy logic in computer-aided breast-cancer diagnosis: analysis of lobulation. Artif Intell Med 11:75–85

    Article  Google Scholar 

  7. Lavanya D, Usha Rani K (2012) Ensemble decision making system for breast cancer data 0975-8887. Int J Comput Appl 51(17):19–23

    Google Scholar 

  8. Lim TS, Loh WY, Shih YS (2000) A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Mach Learn J 40:203–228

    Article  MATH  Google Scholar 

  9. Floares A, Birlutiu A (2012) Decision tree models for developing molecular classifiers for cancer diagnosis. In: WCCI 2012 IEEE World congress on computational intelligence, 10–15 June, Brisbane, Australia

  10. Wang Z, Yu G, Kang Y, Zhao Y, Qu Q (2014) Breast tumor detection in digital mammography based on extreme learning machine. Neurocomputing 128:175–184

    Article  Google Scholar 

  11. Hassanien AE, Kim T, Hassanien AE, Kim T (2012) Breast cancer MRI diagnosis approach using support vector machine and pulse coupled neural networks. J Appl Log 10:277–284

    Article  MathSciNet  Google Scholar 

  12. Keles A, Keles A, Yavuz U (2011) Expert system based on neuro-fuzzy rules for diagnosis breast cancer. Expert Syst Appl 38:5719–5726

    Article  Google Scholar 

  13. Elsayad A (2010) Predicting the severity of breast masses with ensemble of bayesian classifiers, J Comput Sci 6(5):576–584 (ISSN 1549-3636)

    Article  Google Scholar 

  14. Salama GI, Abdelhalim MB, Zeid MA (2012) Breast cancer diagnosis on three different datasets using multiclassifiers. Int J Comput Inf Technol 01(01):764–2277

    Google Scholar 

  15. Angeline Christobel Y, Sivaprakasam P (2011) An empirical comparison of data mining classification methods. Int J Comput Inf Syst 3(2):24–28

    Google Scholar 

  16. Lavanya D, Rani KU (2011) Analysis of feature selection with classification: breast cancer datasets. Indian J Comput Sci Eng (IJCSE) 2(5):756–763

    Google Scholar 

  17. Maglogiannis I, Zafiropoulos E et al (2009) An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers. Appl Intel 30:24–36

    Article  Google Scholar 

  18. Lavanya D, Rani KU (2012) Ensemble decision tree classifier for breast cancer data. Int J Inf Technol Converg Serv (IJITCS) 2(1):17–24

    Google Scholar 

  19. Tu MC, Shin D, Shin D (2009) Effective diagnosis of heart disease through bagging approach. In: 2nd international conference on biomedical engineering and informatics, pp 1–4

  20. Leod PM, Verma B, Zhang M (2014) Optimizing configuration of neural ensemble network for breast cancer diagnosis. Proc Int Jt Conf Neural Netw. https://doi.org/10.1109/IJCNN.2014.6889707,

    Google Scholar 

  21. Jiang M, Zhang S, Li H, Metaxas DN (2015) Computer-aided diagnosis of mammographic masses using scalable image retrieval. IEEE Trans Biomed Eng 62(2):783–792. https://doi.org/10.1109/TBME.2014.2365494

    Article  Google Scholar 

  22. Choi JY, Kim DH, Plataniotis KN, Ro YM (2016) Classifier ensemble generation and selection with multiple feature representations for classification applications in computer-aided detection and diagnosis on mammography. Expert Syst Appl 46:106–121. https://doi.org/10.1016/j.eswa.2015.10.014

    Article  Google Scholar 

  23. Ebrahimpour MK, Mirvaziri H, Sattari-Naeini V (2017) Improving breast cancer classification by dimensional reduction on mammograms. Comput Methods Biomech Biomed Eng Imaging Vis 1163:1–11. https://doi.org/10.1080/21681163.2017.1326847

    Article  Google Scholar 

  24. Li H, Meng X, Wang T, Tang Y, Yin Y (2017) Breast masses in mammography classification with local contour features. Biomed Eng Online 16(1):44. https://doi.org/10.1186/s12938-017-0332-0

    Article  Google Scholar 

  25. Sun W, Tseng TLB, Zhang J, Qian W (2017) Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput Med Imaging Gr 57:4–9

    Article  Google Scholar 

  26. Chougrad H, Zouaki H, Alheyane O (2018) Deep convolutional neural networks for breast cancer screening. Comput Methods Progr Biomed. https://doi.org/10.1016/j.cmpb.2018.01.011

    Google Scholar 

  27. Vinay A, Shekhar VS, Murthy KNB, Natarajan S (2015) Face recognition using Gabor wavelet features with PCA and KPCA—a comparative study. Proc Comput Sci 57:650–659

    Article  Google Scholar 

  28. Shen L, Bai L (2006) A review on Gabor wavelets for face recognition. Pattern Anal Appl 9:273–292

    Article  MathSciNet  Google Scholar 

  29. Manjunath BS (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18:837–842

    Article  Google Scholar 

  30. Wu X et al (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14:1–37

    Article  Google Scholar 

  31. Antipov E, Pokryshevskaya E (2010) Applying CHAID for logistic regression diagnostics and classification accuracy improvement. J Target Meas Anal Mark 18:109–117

    Article  Google Scholar 

  32. Loh W (2008) Classification and regression tree methods. Encycl Stat Qual Reliab. https://doi.org/10.1002/9780470061572,

    Google Scholar 

  33. Ozyildirim BM, Avci M (2016) One pass learning for generalized classifier neural network. Neural Netw 73:70–76

    Article  Google Scholar 

  34. Cano JR (2013) Analysis of data complexity measures for classification. Expert Syst Appl 40(12):4820–4831. https://doi.org/10.1016/j.eswa.2013.02.025

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Ardalan Ghasemzadeh, Saeed Sarbazi Azad or Elham Esmaeili.

Ethics declarations

Conflict of interest

All authors have no conflict of interest.

Additional information

Publisher’s Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghasemzadeh, A., Sarbazi Azad, S. & Esmaeili, E. Breast cancer detection based on Gabor-wavelet transform and machine learning methods. Int. J. Mach. Learn. & Cyber. 10, 1603–1612 (2019). https://doi.org/10.1007/s13042-018-0837-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-018-0837-2

Keywords

Navigation