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Breast cancer detection by using associative classifier with rule refinement method based on relevance feedback

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Abstract

Computer-aided diagnosis system that uses classification process for an automated detection of breast cancer could provide a second opinion that improves diagnosis. Several researchers have proposed the use of associative classifier that generates strong associations between features and reveals hidden relationship that can be missed by other classification algorithms. However, the effectiveness of an associative classifier depends largely on the generalized rules based on training data. Often, the number of training data is limited, which may further produce the classification rules that are stagnant and cannot adapt to a changing distribution of test images, as such it may not produce complete and accurate rules for classification for future cases. This paper aims to address this issue by refining rules that are static using dynamic rule refinement technique. This technique helps to adapt to the changes in the new evidences that can be used for classification to further enhance the performance of the associative classifier. A method named the rule refinement based on incremental modification is proposed that dynamically refines the rules after the suggested term is validated by the experts. Once the initial classification is performed using the generalized rules for each test example, the results are validated using the experts feedback. Based on the validated classification result either correct or incorrect, the rules that are responsible for classification are refined in three phases. These refined rules are used for classification of future test examples that leads to improved prediction accuracy in comparison with the classifier that uses generalized static rules. The performance of the proposed method evaluated on the digital database for screening mammography dataset is promising and has achieved an overall classification accuracy of 96% in the biased setting and an accuracy of 95.24% in the unbiased setting as compared to the accuracy 90.48% of baseline classifier with static rules.

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References

  1. Yusof NM, Isa NAM, Sakim HAM (2007) Computer-aided detection and diagnosis for microcalcifications in mammogram: a review. Int J Comput Sci Netw Secur 7(6):202–208

    Google Scholar 

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

    Article  Google Scholar 

  3. Quinlan JR (2014) C4.5: programs for machine learning. Elsevier, Amsterdam

    Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  6. Rish I, et al. (2001) An empirical study of the naive bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, IBM, New York, vol 3, pp 41–46

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

    Article  Google Scholar 

  8. Watanabe CY, Ribeiro MX, Traina C, Traina AJ (2010) Sacminer: a new classification method based on statistical association rules to mine medical images. In: International conference on enterprise information systems. Springer, pp 249–263

  9. Watanabe CY, Ribeiro MX, Traina AJ, Traina C (2012) A statistical associative classifier with automatic estimation of parameters on computer aided diagnosis. In: 2012 11th international conference on machine learning and applications (ICMLA). IEEE, vol 1, pp 564–567

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

    Article  Google Scholar 

  11. Zhang X, Zhang Y, Han EY, Jacobs N, Han Q, Wang X, Liu J (2017) Whole mammogram image classification with convolutional neural networks. In: 2017 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, pp 700–704

  12. Zeng X, Chen H, Luo Y, Ye W (2019) Automated diabetic retinopathy detection based on binocular siamese-like convolutional neural network. IEEE Access 7:30744–30753

    Article  Google Scholar 

  13. Sze V, Chen YH, Yang TJ, Emer JS (2017) Efficient processing of deep neural networks: a tutorial and survey. Proc IEEE 105(12):2295–2329

    Article  Google Scholar 

  14. Stoutchinin A, Conti F, Benini L (2019) Optimally scheduling cnn convolutions for efficient memory access. arXiv:190201492

  15. Kanchanamani M, Perumal V (2016) Performance evaluation and comparative analysis of various machine learning techniques for diagnosis of breast cancer. Biomed Res 27(3)

  16. Li Y, Chen H, Rohde GK, Yao C, Cheng L (2015) Texton analysis for mass classification in mammograms. Pattern Recognit Lett 52:87–93

    Article  Google Scholar 

  17. Miranda GHB, Felipe JC (2015) Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization. Comput Biol Med 64:334–346

    Article  Google Scholar 

  18. Ertosun MG, Rubin DL (2015) Probabilistic visual search for masses within mammography images using deep learning. In: 2015 IEEE international conference on bioinformatics and biomedicine (BIBM). IEEE, pp 1310–1315

  19. Qiu Y, Wang Y, Yan S, Tan M, Cheng S, Liu H, Zheng B (2016a) An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology. In: Medical imaging 2016: computer-aided diagnosis, international society for optics and photonics, vol 9785, p 978521

  20. Qiu Y, Yan S, Tan M, Cheng S, Liu H, Zheng B (2016b) Computer-aided classification of mammographic masses using the deep learning technology: a preliminary study. In: Medical imaging 2016: computer-aided diagnosis, international society for optics and photonics, vol 9785, p 978520

  21. Wang J, Yang X, Cai H, Tan W, Jin C, Li L (2016) Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci Rep 6:27327

    Article  Google Scholar 

  22. Jiao Z, Gao X, Wang Y, Li J (2016) A deep feature based framework for breast masses classification. Neurocomputing 197:221–231

    Article  Google Scholar 

  23. Abbas Q (2016) Deepcad: a computer-aided diagnosis system for mammographic masses using deep invariant features. Computers 5(4):28

    Article  Google Scholar 

  24. Ma Blwhy, Liu B (1998) Integrating classification and association rule mining. In: Proceedings of the fourth international conference on knowledge discovery and data mining

  25. Sonar P, Bhosle U (2017) Optimization of association rule mining for mammogram classification. Int J Image Process (IJIP) 11(3):67

    Google Scholar 

  26. Li W, Han J, Pei J (2001) Cmar: accurate and efficient classification based on multiple class-association rules. In: icdm. IEEE, p 369

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

    Article  MathSciNet  Google Scholar 

  28. Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: Acm sigmod record, ACM 22:207–216

  29. Ribeiro MX, Traina AJ, Balan AG, Traina Jr C, Marques PM (2007) Sugar: a framework to support mammogram diagnosis. In: Twentieth IEEE international symposium on computer-based medical systems, 2007. CBMS’07. IEEE, pp 47–52

  30. Yun J, Zhanhuai L, Yong W, Longbo Z (2005) Joining associative classifier for medical images. In: 2005. HIS’05. Fifth international conference on hybrid intelligent systems. IEEE, pp 6–pp

  31. Tseng VS, Wang MH, Su JH (2005) A new method for image classification by using multilevel association rules. In: 2005. 21st international conference on data engineering workshops. IEEE, pp 1180–1180

  32. Almasi M, Abadeh MS (2020) Cars-lands: an associative classifier for large-scale datasets. Pattern Recognit 100:107128

    Article  Google Scholar 

  33. De La Vega ARD, Villuendas-Rey Y, Yáñez-Márquez C, Camacho-Nieto O (2020) The naïve associative classifier with epsilon disambiguation. IEEE Access 8:51862–51870

    Article  Google Scholar 

  34. Sood N, Bindra L, Zaiane O (2020) Bi-level associative classifier using automatic learning on rules. In: International conference on database and expert systems applications. Springer, pp 201–216

  35. Rajeswari A, Deisy C (2019) Fuzzy logic based associative classifier for slow learners prediction. J Intell Fuzzy Syst 36(3):2691–2704

    Article  Google Scholar 

  36. Villuendas-Rey Y, Yáñez-Márquez C, Anton-Vargas JA, López-Yáñez I (2019) An extension of the gamma associative classifier for dealing with hybrid data. IEEE Access 7:64198–64205

    Article  Google Scholar 

  37. Zhai C, Li Z, Jiang F, Ma JJ, Xu Z (2020) A spatial analysis methodology based on lazy ensembled adaptive associative classifier and gis for examining the influential factors on traffic fatalities. IEEE Access 8:117932–117945

    Article  Google Scholar 

  38. Villuendas-Rey Y, Hernández-Castaño JA, Camacho-Nieto O, Yáñez-Márquez C, López-Yañez I (2019) Nacod: a naïve associative classifier for online data. IEEE Access 7:117761–117767

    Article  Google Scholar 

  39. Rodda S, Shashi M A new approach to associative classifier development for imbalanced datasets. Int J Comput Appl Eng Technol Sci 2(1)

  40. Wu CH, Wang JY, Chen CJ (2012) Mining condensed rules for associative classification. In: 2012 international conference on machine learning and cybernetics (ICMLC). IEEE, vol 4, pp 1565–1570

  41. Abubacker NF, Azman A, Murad MAA, Doraisamy S (2017) An improved peripheral enhancement of mammogram images by using filtered region growing segmentation. J Theor Appl Inf Technol 95(14)

  42. Abubacker NF, Azman A, Doraisamy S, Murad MAA, Elmanna MEM, Saravanan R (2014) Correlation-based feature selection for association rule mining in semantic annotation of mammographic medical images. In: Asia information retrieval symposium. Springer, pp 482–493

  43. Debelee TG, Gebreselasie A, Schwenker F, Amirian M, Yohannes D (2019) Classification of mammograms using texture and cnn based extracted features. J Biomimetics Biomater Biomed Eng Trans Tech Publ 42:79–97

    Article  Google Scholar 

  44. Mohanty F, Rup S, Dash B, Majhi B, Swamy M (2019) Digital mammogram classification using 2d-bdwt and glcm features with foa-based feature selection approach. Neural Comput Appl, pp 1–15

  45. Kaklotar A (2019) Research on different feature extraction and mammogram classification techniques. Indian J Appl Res 9(12)

  46. Bhosle U, Deshmukh J (2019) Mammogram classification using adaboost with rbfsvm and hybrid knn-rbfsvm as base estimator by adaptively adjusting \(\gamma\) and c value. Int J Inf Technol 11(4):719–726

    Google Scholar 

  47. Jaleel JA, Salim S, Archana S (2014) Textural features based computer aided diagnostic system for mammogram mass classification. In: 2014 international conference on control, instrumentation, communication and computational technologies (ICCICCT). IEEE, pp 806–811

  48. Tai SC, Chen ZS, Tsai WT (2014) An automatic mass detection system in mammograms based on complex texture features. IEEE J Biomed Health Informatics 18(2):618–627

    Article  Google Scholar 

  49. Haralick RM, Shanmugam K, Dinstein I et al (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621

    Article  Google Scholar 

  50. Hu MK (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8(2):179–187

    Article  MATH  Google Scholar 

  51. Ghasemzadeh A, Azad SS, Esmaeili E (2019) Breast cancer detection based on gabor-wavelet transform and machine learning methods. Int J Mach Learn Cybern 10(7):1603–1612

    Article  Google Scholar 

  52. Dougherty J, Kohavi R, Sahami M (1995) Supervised and unsupervised discretization of continuous features. In: Machine learning proceedings 1995. Elsevier, pp 194–202

  53. Cebeci Z, Yıldız F (2017) Unsupervised discretization of continuous variables in a chicken egg quality traits dataset. Turk J Agricult-Food Sci Technol 5(4):315–320

    Article  Google Scholar 

  54. Dimić G, Rančić D, Milentijević I, Spalević P (2018) Improvement of the accuracy of prediction using unsupervised discretization method: educational data set case study. Tehnički vjesnik 25(2):407–414

    Google Scholar 

  55. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. ACM SIGKDD Explor Newslett 11(1):10–18

    Article  Google Scholar 

  56. Abubacker NF, Azman A, Murad MAA, Doraisamy S (2016) Adaptive associative classifier for mammogram classification. In: Proceedings of SAI intelligent systems conference. Springer, pp 721–736

  57. Rose C, Turi D, Williams A, Wolstencroft K, Taylor C (2006) Web services for the ddsm and digital mammography research. In: International workshop on digital mammography. Springer, pp 376–383

  58. Metz CE (2008) Roc analysis in medical imaging: a tutorial review of the literature. Radiol Phys Technol 1(1):2–12

    Article  Google Scholar 

  59. Fangyu L, Hua H (2018) Assessing the accuracy of diagnostic tests. Shanghai Arch Psych 30(3):207

    Google Scholar 

  60. Shen L, Margolies LR, Rothstein JH, Fluder E, McBride RB, Sieh W (2017) Deep learning to improve breast cancer early detection on screening mammography. arXiv:170809427

  61. Deshmukh J, Bhosle U (2017) Glcm based improved mammogram classification using associative classifier. Int J Image Graph Signal Process 11(7):66

    Article  Google Scholar 

  62. Sonar P, Bhosle U, Choudhury C (2017) Mammography classification using modified hybrid svm-knn. In: 2017 international conference on signal processing and communication (ICSPC). IEEE, pp 305–311

  63. Zhu W, Xiang X, Tran TD, Hager GD, Xie X (2018) Adversarial deep structured nets for mass segmentation from mammograms. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE, pp 847–850

  64. Tavakoli N, Karimi M, Norouzi A, Karimi N, Samavi S, Soroushmehr SR (2019) Detection of abnormalities in mammograms using deep features. J Ambient Intell Human Comput pp 1–13

  65. Yang C, Shi Z (2019) Research in breast cancer imaging diagnosis based on regularized lightgbm. In: Cyberspace data and intelligence, and cyber-living, syndrome, and health. Springer, pp 487–503

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Authors and Affiliations

  1. Computer & Network Engineering(CIT), United Arab Emirates University, Al Ain, Abudhabi, United Arab Emirates

    Nirase Fathima Abubacker

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

    Azreen Azman, Shyamala Doraisamy & Masrah Azrifah Azmi Murad

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  1. Nirase Fathima Abubacker
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  2. Azreen Azman
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  3. Shyamala Doraisamy
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  4. Masrah Azrifah Azmi Murad
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Correspondence to Nirase Fathima Abubacker.

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Abubacker, N.F., Azman, A., Doraisamy, S. et al. Breast cancer detection by using associative classifier with rule refinement method based on relevance feedback. Neural Comput & Applic 34, 16897–16910 (2022). https://doi.org/10.1007/s00521-022-07336-9

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