Skip to main content
SpringerLink
Log in

Adaptive Associative Classifier for Mammogram Classification

  • Conference paper
  • First Online:
Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (IntelliSys 2016)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 15))

Included in the following conference series:

Abstract

Computer-aided diagnosis (CADx) can help radiologists in the interpretation of mammograms to assist them in diagnostic decision-making. Such a system is capable to automatically classify and suggest the pathological terms for a new mammography image. A few traditional classification methods have shown poor performance for nonlinear separable data. Thus, there is a need to use an effective classifier that generates strong associations between features and reveals hidden relationship that can be missed by other classification algorithms. The existing associative classification techniques such as HiCARe, SACMINER, MINSAR, and ACHAvC are able to solve the problem. However, once the classifiers are built, they do not adapt to the changes in the database over the time. In the real application, the radiologists or the doctors are capable of making feedback to the system through either accepting the suggested diagnostic terms when it is accurate or to choose the correct terms when it is inaccurate. Therefore, in this paper, a method to improve the classification of mammogram images is proposed through refinement of the association rules by using the feedback from the users. A rule refinement method called RRIM is integrated into ACHAvC associative classifier and the performance is evaluated on 600 mammogram images from DDSM. The result is encouraging, showing that RRIM is able to obtain 0.96 in classification accuracy, outperforms the baseline and also other classification methods.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Yusof, N.M., Isa, N.A., Sakim, H.A.: Computer-aided detection and diagnosis for microcalcifications in Mammogram: @upm.edu.mya review. Int. J. Comput. Sci. Netw. Secur. 7, 202–208 (2007)

    Google Scholar 

  2. Chan, H.-P., Wei, D., Helvie, M.A., Sahiner, B., Adler, D.D., Goodsitt, M.M., Petrick, N.: Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space. Phys. Med. Biol. 40(5), 857 (1995)

    Article  Google Scholar 

  3. J. R. Quinlan, C4. 5: programs for machine learning. Elsevier, 2014

    Google Scholar 

  4. Jensen, D.D., Cohen, P.R.: Multiple comparisons in induction algorithms. Mach. Learn. 38(3), 309–338 (2000)

    Article  MATH  Google Scholar 

  5. Park, J., Sandberg, I.W.: Universal approximation using radial-basis-function networks. Neural Comput. 3(2), 246–257 (1991)

    Article  Google Scholar 

  6. Rish, I.: An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, no. 22, pp. 41–46 (2001)

    Google Scholar 

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

    Article  Google Scholar 

  8. Watanabe, C.Y.V., Ribeiro, M.X., Traina, C., Traina, A.J.M.: SACMiner: a new classification method based on statistical association rules to mine medical images. In: Enterprise Information Systems, Springer, Berlin, pp. 249–263 (2010)

    Google Scholar 

  9. Watanabe, C.Y.V., Ribeiro, M.X., Traina, A.J.M., Traina, C.: A statistical associative classifier with automatic estimation of parameters on computer aided diagnosis. In: 2012 11th International Conference on Machine Learning and Applications (ICMLA), vol. 1, pp. 564–567 (2012)

    Google Scholar 

  10. Abubacker, N.F., Azman, A., Murad, M.A.A., Doraisamy, S.: Effective rule based classifier using multivariate filter and genetic miner for mammographic image classification. Res. J. Appl. Sci. Eng. Technol. 10(5), 591–598 (2015)

    Google Scholar 

  11. Chowdhary, C., Acharjya, D.: Breast Cancer Detection using Intuitionistic Fuzzy Histogram Hyperbolization and Possibilitic Fuzzy c-mean Clustering algorithms with texture feature based Classification on Mammography Images. In: Proceedings of the International Conference on Advances in Information Communication Technology & Computing, p. 21. ACM, August 2016

    Google Scholar 

  12. Mazurowski, M.A., Lo, J.Y., Harrawood, B.P., Tourassi, G.D.: Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis. J. Biomed. Inform. 44(5), 815–823 (2011). View at Publisher · View at Google Scholar · View at Scopus

    Google Scholar 

  13. Suganthi, M., Madheswaran, M.: An enhanced decision support system for breast tumor identification in screening mammograms using combined classifier. In: Proceedings of the International Conference and Workshop on Emerging Trends in Technology - ICWET 2010 (2010)

    Google Scholar 

  14. Xu, W., Liu, W., Li, L., Shao, G., Zhang, J.: Identification of masses and microcalcifications in the mammograms based on three neural networks: comparison and discussion. In: Proceedings of the 2nd International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2008), pp. 2299–2302, May 2008. View at Publisher · View at Google Scholar · View at Scopus

    Google Scholar 

  15. Deserno, T.M., Soiron, M., de Oliveira, J.E.E., de Araújo, A.A.: Towards computer-aided diagnostics of screening mammography using content-based image retrieval. In: Proceedings of the 24th SIBGRAPI Conference on Graphics, Patterns and Images, pp. 211–219, Maceió, Brazil, August 2011. View at Publisher · View at Google Scholar · View at Scopus

    Google Scholar 

  16. Deepa, S., Bharathi, V.: An efficient digital mammogram imageclassification using DTCWT and SVM. In: Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology - CCSEIT 2012 (2012)

    Google Scholar 

  17. Lesniak, J., Hupse, R., Kallenberg, M., et al.: Computer aided detection of breast masses in mammography using support vector machine classification. In: Proceedings of the Medical Imaging 2011: Computer-Aided Diagnosis (2011)

    Google Scholar 

  18. Wei, C.-H., Li, Y., Huang, P.J.: Mammogram retrieval through machine learning within BI-RADS standards. J. Biomed. Inform. 44(4), 607–614 (2011). View at Publisher · View at Google Scholar · View at Scopus

    Google Scholar 

  19. Ma, B.L.W.H.Y.: Integrating classification and association rule mining. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (1998)

    Google Scholar 

  20. Li, W., Han, J., Pei, J.: CMAR: accurate and efficient classification based on multiple class-association rules. In: ICDM 2001, Proceedings IEEE International Conference on Data Mining, 2001, pp. 369–376 (2001)

    Google Scholar 

  21. Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Min. Knowl. Discov. 15(1), 55–86 (2007)

    Article  MathSciNet  Google Scholar 

  22. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22(2), 207–216 (1993)

    Article  Google Scholar 

  23. Ribeiro, M.X., Traina, A.J.M., Balan, A.G.R., Traina, C., Marques, P.: SuGAR: a framework to support mammogram diagnosis. In: Twentieth IEEE International Symposium on Computer-Based Medical Systems, 2007. CBMS 2007, pp. 47–52 (2007)

    Google Scholar 

  24. Yun, J., Zhanhuai, L., Yong, W., Longbo, Z.: Joining associative classifier for medical images. In: Fifth International Conference on Hybrid Intelligent Systems, 2005. HIS’05, p. 6 (2005)

    Google Scholar 

  25. Tseng, V.S., Wang, M.-H., Su, J.-H.: A new method for image classification by using multilevel association rules. In: 21st International Conference on Data Engineering Workshops, 2005, p. 1180 (2005)

    Google Scholar 

  26. Wu, C., Wang, J., Chen, C.: Mining condensed rules for associative classification. In: 2012 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 4, pp. 1565–1570 (2012)

    Google Scholar 

  27. Ayan, N.F., Tansel, A.U., Arkun, E.: An efficient algorithm to update large itemsets with early pruning. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 287–291 (1999)

    Google Scholar 

  28. Lee, C.-H., Lin, C.-R., Chen, M.-S.: Sliding-window filtering: an efficient algorithm for incremental mining. In: Proceedings of the Tenth International Conference on Information and Knowledge Management, pp. 263–270 (2001)

    Google Scholar 

  29. Veloso, A., Meira Jr., W., de Carvalho, M., Pôssas, B., Parthasarathy, S., Zaki, M.J.: Mining frequent itemsets in evolving databases. In: SDM, pp. 494–510 (2002)

    Google Scholar 

  30. Chang, C.-H., Yang, S.-H.: Enhancing SWF for incremental association mining by itemset maintenance. In: Advances in Knowledge Discovery and Data Mining, Springer, pp. 301–312 (2003)

    Google Scholar 

  31. Teng, W.-G., Chen, M.-S.: Incremental mining on association rules. In: Foundations and Advances in Data Mining, Springer, pp. 125–162 (2005)

    Google Scholar 

  32. Cheung, D.W.-L., Lee, S.D., Kao, B., et al.: A general incremental technique for maintaining discovered association rules. In: DASFAA, vol. 6, pp. 185–194 (1997)

    Google Scholar 

  33. Toivonen, H., et al.: Sampling large databases for association rules. In: VLDB, vol. 96, pp. 134–145 (1996)

    Google Scholar 

  34. Hong, T.-P., Wang, C.-Y., Tao, Y.-H.: A new incremental data mining algorithm using pre-large itemsets. Intell. Data Anal. 5(2), 111–129 (2001)

    MATH  Google Scholar 

  35. Lin, C.-W., Hong, T.-P., Lu, W.-H.: The Pre-FUFP algorithm for incremental mining. Expert Syst. Appl. 36(5), 9498–9505 (2009)

    Article  Google Scholar 

  36. Le, T.-P., Hong, T.-P., Vo, B., Le, B.: Incremental mining frequent itemsets based on the trie structure and the pre-large itemsets. In: 2011 IEEE International Conference on Granular Computing (GrC), pp. 369–373 (2011)

    Google Scholar 

  37. Thomas, S., Bodagala, S., Alsabti, K., Ranka, S.: An efficient algorithm for the incremental updation of association rules in large databases. In: KDD, pp. 263–266 (1997)

    Google Scholar 

  38. Adnan, M., Alhajj, R., Barker, K.: Performance analysis of incremental update of association rules mining approaches. In: Proceedings. 2005 IEEE International Conference on Intelligent Engineering Systems, 2005. INES’05, pp. 129–134 (2005)

    Google Scholar 

  39. Jaleel, J.A., Salim, S., Archana, S.: Textural features based computer aided diagnostic system for mammogram mass classification. In: 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 806–811 (2014)

    Google Scholar 

  40. Tai, S.-C., Chen, Z.-S., Tsai, W.-T.: An automatic mass detection system in mammograms based on complex texture features. IEEE J. Biomed. Heal. Inform. 18(2), 618–627 (2014)

    Article  Google Scholar 

  41. Hu, M.-K.: Visual pattern recognition by moment invariants. Inf. Theory IRE Trans. 8(2), 179–187 (1962)

    Article  MATH  Google Scholar 

  42. Dougherty, J., Kohavi, R., Sahami, M. et al.: Supervised and unsupervised discretization of continuous features. In: Machine Learning: Proceedings of the Twelfth International Conference, vol. 12, pp. 194–202 (1995)

    Google Scholar 

  43. Holmes, G., Donkin, A., Witten, I.H.: Weka: a machine learning workbench. In: Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems, 1994, pp. 357–361 (1994)

    Google Scholar 

  44. Hall, M.A.: Correlation-Based Feature Selection for Machine Learning. The University of Waikato (1999)

    Google Scholar 

  45. Abubacker, N.F., Azman, A., Doraisamy, S., Murad, M.A.A., Elmanna, M.E.M., Saravanan, R.: Correlation-based feature selection for association rule mining in semantic annotation of mammographic medical images. In: Information Retrieval Technology, Springer, pp. 482–493 (2014)

    Google Scholar 

  46. Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of 28th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computing, Engineering and Technology, Asia Pacific University of Technology & Innovation, 57000, Bukit Jalil, Kuala Lumpur, Malaysia

    Nirase Fathima Abubacker

  2. Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400, Serdang, Malaysia

    Azreen Azman, Masrah Azrifah Azmi Murad & Shyamala Doraisamy

Authors
  1. Nirase Fathima Abubacker
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Azreen Azman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Masrah Azrifah Azmi Murad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shyamala Doraisamy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Azreen Azman .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Engineering, School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Abubacker, N.F., Azman, A., Murad, M.A.A., Doraisamy, S. (2018). Adaptive Associative Classifier for Mammogram Classification. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-56994-9_49

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-56994-9_49

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56993-2

  • Online ISBN: 978-3-319-56994-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation