Skip to main content

Advertisement

SpringerLink
Log in

WARM: a new breast masses classification method by weighting association rule mining

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

Abstract

Breast cancer is the growth of a malignant tumor in the breast. The incidence of this disease in women has increased significantly in recent years. Currently, early detection is an important factor in cancer treatment. The most effective method for early detection is through mammography’s images. The computer-aided diagnosis systems are essential to help searching for suspicious signs, or classifying lesions in benign or malignant types. In this paper, a new method is designed for mass detection and classification based on weighted association rule mining (WARM). The main purpose of this study is to focus on the segmentation and classification and to provide a solution to optimize the accuracy of detection and classification of masses in mammography images to classify the masses in mammography images into two classes, benign and malignant. The results show proposed model in terms of accuracy, sensitivity and specificity achieved superior in comparison with several baselines.

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

Similar content being viewed by others

References

  1. Gardezi, S.J.S., Elazab, A., Lei, B., Wang, T.: Breast cancer detection and diagnosis using mammographic data: systematic review. J. Med. Internet Res. 21(7), e14464 (2019)

    Article  Google Scholar 

  2. Gnanasekaran, V.S., Joypaul, S., Sundaram, P.M.: A survey on machine learning algorithms for the diagnosis of breast masses with mammograms. Curr. Med. Imaging 16, 639–652 (2020)

    Article  Google Scholar 

  3. Lbachir, I.A., Es Salhi, R., Daoudi, I., Tallal, S., Medromi, H.: A survey on segmentation techniques of mammogram images. In: Lecture Notes in Electrical Engineering Advances in Ubiquitous Networking, vol. 2, pp. 545–556 (2016)

  4. Khan, M.H.-M.: Automated breast cancer diagnosis using artificial neural network (ANN). In: 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (2017)

  5. Mahdikhani, L., Keyvanpour, M.R.: Challenges of Data mining classification techniques in mammograms. In: 5th Conference on Knowledge Based Engineering and Innovation, Tehran, Iran, pp. 637–643 (2019)

  6. Parashar, N.: Classification using association rule mining over mammogram images: a review. Int. J. Emerg. Technol. 11(2), 954–957 (2020)

    Google Scholar 

  7. Bassett, L.W., Conner, K., MS, IV.: The abnormal mammogram. In: Kufe, D.W., Pollock, R.E., Weichselbaum, R.R., et al. (eds.) Holland-Frei Cancer Medicine. 6th edn. BC Decker, Hamilton (2003)

  8. Fajri Riesaputri, D., Atika Sari, C., De Rosal, I.M.S., Hari Rachmawanto, E.: Classification of breast cancer using PNN classifier based on GLCM feature extraction and GMM segmentation. In: 2020 International Seminar on Application for Technology of Information and Communication (iSemantic), pp. 83–87 (2020)

  9. Khasana, U., Sigit, R., Yuniarti, H.: Segmentation of Breast using ultrasound image for detection breast cancer. In: 2020 International Electronics Symposium (IES), pp. 584–587 (2020)

  10. You, X., Peng, Q., Yuan, Y., Cheung, Y.-M., Lei, J.: Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recognit. 44(10–11), 2314–2324 (2011)

    Article  Google Scholar 

  11. Mohamed, B.A., Salem, N.M.: Automatic classification of masses from digital mammograms. In: 35th National Radio Science Conference (2018)

  12. Tan, Y.J., Sim, K.S., Ting, F.F.: Breast cancer detection using convolutional neural networks for mammogram imaging system. In: International Conference on Robotics, Automation and Sciences (2017)

  13. Thawkar, S., Ingolikar, R.: Automatic detection and classification of masses in digital mammograms. Int. J. Intell. Eng. Syst. 10, 65–74 (2017)

    Google Scholar 

  14. Mohammed, A., Ahmed, A., Mohammed, W., Viju, G.K., Taha, M.: Mammogram images classification using linear discriminant analysis technique. Int. Res. J. Eng. Technol. 07(06) (2020)

  15. Kanchanamani, M., Perumal, P.: Performance evaluation and comparative analysis of various machine learning techniques for diagnosis of breast cancer. Proc. Biomed. Res. (2016)

  16. Basheer, N.M., Mohammed, M.H.: Classification of breast masses in digital mammograms using support vector machines. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(10), 57–63 (2013)

    Google Scholar 

  17. Medeiros, A., Ohata, E.F., Silva, F.H.S., Rego, P.A.L., Reboucas Filho, P.P.: An approach to BI-RADS uncertainty levels classification via deep learning with transfer learning technique. In: IEEE 33rd International Symposium on Computer-Based Medical Systems, Rochester, MN, USA, pp. 603–608 (2020)

  18. Benhassine, N.E., Boukaache, A., Boudjehem, D.: A new cad system for breast cancer classification using discrimination power analysis Of Wavelet’s coefficients and support vector machine. J. Mech. Med. Biol. 20(06) (2020)

  19. Ahmed Ali, H.A., Alobe Donnish, M.A.: Classification method for mammogram image using the decision tree techniques. J. Math. Comput. Sci. Res. 4(1), 001–005 (2018)

  20. Alam, N., Denton, E.R.E., Zwiggelaar, R.: Classification of microcalcification clusters in digital mammograms using a stack generalization based classifier. J. Imaging (2019)

  21. Rajaguru, H., Chakravarthy, S.R.S.: Analysis of decision tree and K-nearest neighbor algorithm in the classification of breast cancer. Asian Pac. J. Cancer Prev. 20(12), 3777–3781 (2019)

  22. Zhili, C., Strange, H., Oliver, A., et al.: Topological modeling and classification of mammographic macrocalcification clusters. IEEE Trans. Biomed. Eng. 62(4), 1203–1214 (2015)

    Article  Google Scholar 

  23. Rajkumar, K.K.: Detection and classification of mammogram images using K-means and fuzzy C-means clustering algorithm with texture features. Int. J. Appl. Eng. Res.14 (2019)

  24. Parvathavarthini, S., Karthikeyani Visalakshi, N., Shanthi, S.: Breast cancer detection using crow search optimization based intuitionistic fuzzy clustering with neighborhood attraction. Asian Pac. J. Cancer Prev. 20(1), 157–165 (2019)

    Article  Google Scholar 

  25. Antonie, M.L., Zaïane, O.R., Coman, A.: Associative classifiers for medical images. In: Mining Multimedia and Complex Data Lecture Notes in Computer Science, pp. 68–83 (2003)

  26. Dua, S., Singh, H., Thompson, H.: Associative classification of mammograms using weighted rules. Expert Syst. Appl. 36(5), 9250–9259 (2009)

    Article  Google Scholar 

  27. Jiang, Y., Li, Z.-H., Wang, Y., Zhang, Z.-B.: Joining associative classifier for medical images. In: Fifth International Conference on Hybrid Intelligent Systems (HIS’05), pp. 367–372 (2005)

  28. Sonar, P., Bhosle, U.: Optimization of association rule mining for mammogram classification. Int. J. Image Process. 11(3) (2017)

  29. Belkhodja, L., Hamdadou, D.: IMCAD: computer aided system for breast masses detection based on immune recognition. Int. J. Interact. Multimed. Artif. Intell. 5(5), 97–108 (2019)

    Google Scholar 

  30. Tian, H.Y., Li, S.J., Wu, T.Q., Yao, M.: An extreme learning machine based on artificial immune system. In: Computational Intelligence and Neuroscience (2018)

  31. Keyvanpour, M.R., Shirzad, M.B., Khoshouei, E.M.: An association rule hiding method based on grouping and weighting. U.P.B. Sci. Bull. Seri. C 82(1) (2020)

  32. Anitha, J., Peter, J.D.: Mammogram segmentation using maximal cell strength updation in cellular automata. Med. Biol. Eng. Comput. 53(8), 737–749 (2015)

    Article  Google Scholar 

  33. Padhi, S., Rup, S., Saxena, S., Mohanty, F.: Mammogram segmentation methods: a brief review. In: 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT), Jaipur, India, pp. 218–223 (2019)

  34. Shrivastava, N., Bharti, J.: Breast Tumor Detection in Digital Mammogram Based on Efficient Seed Region Growing Segmentation. IETE J. Res. (2020)

  35. Nagarajan, V., Britto, E.C., Veeraputhiran, S.M.: Feature extraction based on empirical mode decomposition for automatic mass classification of mammogram images. Med. Novel Technol. Devices 1, 100004 (2019)

    Article  Google Scholar 

  36. Deshmukh, J., Bhosle, U.: Image mining using association rule for medical image dataset. Procedia Comput. Sci. 85, 117–124 (2016)

    Article  Google Scholar 

  37. Ribeiro, M.X., Traina, A.J.M., Traina, C., Azevedo-Marques, P.M.: An association rule-based method to support medical image diagnosis with efficiency. IEEE Trans. Multimed. 10(2), 277–285 (2008)

    Article  Google Scholar 

  38. http://www.wiau.man.ac.uk/services/MIAS/MIASweb.html

  39. Naveed, N., Choi, T., Jaffar, A.: Malignancy and abnormality detection of mammograms using DWT features and ensembling of classifiers. Int. J. Phys. Sci. 6(8), 2107–2116 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Alzahra University, Tehran, Iran

    Mohammad Reza Keyvanpour & Leyli Mahdikhani

  2. Data Mining Laboratory, Department of Computer Engineering, Alzahra University, Tehran, Iran

    Mehrnoush Barani Shirzad

Authors
  1. Mohammad Reza Keyvanpour
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehrnoush Barani Shirzad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Leyli Mahdikhani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Reza Keyvanpour.

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

Keyvanpour, M.R., Barani Shirzad, M. & Mahdikhani, L. WARM: a new breast masses classification method by weighting association rule mining. SIViP 16, 481–488 (2022). https://doi.org/10.1007/s11760-021-01989-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-021-01989-0

Keywords

Search

Navigation