Abstract
Computer-aided diagnosis has gained a significant attention in helping radiologists to improve the accuracy of mammographic detection and diagnostic decision. The aim of proposed research work is to build an efficient and accurate classifier for the classification of mammogram images using a hybrid method by incorporating Genetic Association Rule Miner (GARM) with the learning capability of neural network. A set of features is extracted that comprises of 34 features based on the second and third level of the wavelet decomposition with 13 features measured directly from the gray-level co-occurrence matrix. In order to eliminate the inappropriate features and to increase the efficiency mining process, a multivariate filter is used for feature selection. Based on the selected features, an association rule mining based on modified GARM is used to generate association rules. In the classification phase, the newly generated association rules are used as the input for the creation and training of an artificial neural network. Furthermore, an extended associative classifier using fuzzy feed-forward backpropagation neural network (ACFNN) is proposed as an effective classifier in the context of mammography. The proposed ACFNN performance is compared with associative classifier using feed-forward backpropagation neural network (ACNN). Based on the experimental results, the performance of the proposed ACFNN is improved significantly. Furthermore, it can be inferred that the mammogram classification is better by using ACFNN with accuracy of 95.1 % as compared to ACNN with 93.7 %.
Similar content being viewed by others
References
Evans JA (1994) Screening mammography breast cancer diagnosis in asymptomatic women: Scane, Potchen, Sierra, Azavedo. 1993 Mosby. £ 110. pp 546. ISBN 0 8016 64888
Yu L, Liu H (2003) Feature selection for high-dimensional data: a fast correlation-based filter solution. In: ICML, vol 3, pp 856–863
Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press, Ann Arbor
Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99
Hou ES, Ansari N, Ren H (1994) A genetic algorithm for multiprocessor scheduling. IEEE Trans Parallel Distrib Syst 5(2):113–120
Davis L (1990). Hybrid genetic algorithms for machine learning. In: IEE colloquium on machine learning. IET, pp 9–11
Vafaie H, De Jong K (1992) Genetic algorithms as a tool for feature selection in machine learning. In: Proceedings., Fourth international conference on tools with artificial intelligence, 1992. TAI’92. IEEE, pp 200–203
Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer M, Deb K, Rudolph G, Yao X, Lutton E, Merelo JJ, Schwefel H-P (eds) Parallel problem solving from nature PPSN VI. Springer, Berlin, pp 849–858
Dias AH, De Vasconcelos JA (2002) Multiobjective genetic algorithms applied to solve optimization problems. IEEE Trans Magn 38(2):1133–1136
Tsai CF, Tsai CW, Chen CP, Lin FC (2002) A multiple-searching approach to genetic algorithms for solving traveling salesman problem. In: JCIS, pp 362–366
Saggar M, Agrawal AK, Lad A (2004) Optimization of association rule mining using improved genetic algorithms. In: 2004 IEEE international conference on systems, man and cybernetics, vol 4. IEEE, pp 3725–3729
Ghosh A, Nath B (2004) Multi-objective rule mining using genetic algorithms. Inf Sci 163(1):123–133
Shrivastava VK, Kumar P, Pardasani KR (2010) Extraction of interesting association rules using GA optimization. Global J Comput Sci Technol 10(5):81–84
Jain N, Sharma V, Malviya M (2012) Reduction of negative and positive association rule mining and maintain superiority of rule using modified genetic algorithm. Int J Adv Comput Res (IJACR) 2(4):6
Lim AH, Lee CS, Raman M (2012) Hybrid genetic algorithm and association rules for mining workflow best practices. Expert Syst Appl 39(12):10544–10551
Wakabi-Waiswa PP, Baryamureeba V, Sarukesi K (2011) Optimized association rule mining with genetic algorithms. In: 2011 seventh international conference on natural computation (ICNC), vol 2. IEEE, pp 1116–1120
Nahar J, Imam T, Tickle KS, Chen YPP (2013) Association rule mining to detect factors which contribute to heart disease in males and females. Expert Syst Appl 40(4):1086–1093
Das S, Saha B (2009) Data quality mining using genetic algorithm. Int J Comput Sci Secur 3(2):105–112
Lee DG, Ryu KS, Bashir M, Bae JW, Ryu KH (2013) Discovering medical knowledge using association rule mining in young adults with acute myocardial infarction. J Med Syst 37(2):1–10
Kumar MR, Iyakutti DK (2011) Application of genetic algorithms for the prioritization of association rules. In: IJCA special issue on artificial intelligence techniques-novel approaches and practical applications, pp 1–3
Al-Maqaleh BM (2013) Discovering interesting association rules: a multi-objective genetic algorithm approach. Int J Appl Inf Syst 5(3):47–52
Vizhi JM, Bhuvaneswari DT (2012) Data quality measurement with threshold using genetic algorithm. Int J Eng Res Appl 2(4):1197-120
Keshavamurthy BN, Khan AM, Toshniwal D (2013) Privacy preserving association rule mining over distributed databases using genetic algorithm. Neural Comput Appl 22(1):351–364
Islam MJ, Ahmadi M, Sid-Ahmed MA (2010) An efficient automatic mass classification method in digitized mammograms using artificial neural network. arXiv preprint arXiv:1007.5129
Marcano-Cedeno A, Andina D (2012) Data mining for the diagnosis of type 2 diabetes. In: World Automation Congress (WAC), 2012. IEEE, pp 1–6
Khashei M, Hamadani AZ, Bijari M (2012) A fuzzy intelligent approach to the classification problem in gene expression data analysis. Knowl Based Syst 27:465–474
Ni X (2008) Research of data mining based on neural networks. World Acad Sci Eng Technol 39:381–384
McInerney M, Dhawan AP (1993) Use of genetic algorithms with backpropagation in training of feedforward neural networks. In: IEEE international conference on neural networks, 1993. IEEE, pp 203–208
Shekhawat PB, Dhande SS (2011) A classification technique using associative classification. Int J Comput Appl 20(5):20–28
Mathew LS (2013) Integrated associative classification and neural network model enhanced by using astatistical approach. Int J Data Min Knowl Manag Process 3(4):107
Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610–621
Kang G (1977) Digital image processing. Quest 1:2–20
Dougherty J, Kohavi R, Sahami M (1995) Supervised and unsupervised discretization of continuous features. In: Machine learning: proceedings of the twelfth international conference, vol 12, pp 194–202
Holmes G, Donkin A, Witten IH (1994) Weka: a machine learning workbench. In: Proceedings of the 1994 Second Australian and New Zealand conference on intelligent information systems, 1994. IEEE, pp 357–361
Liu H, Motoda H (eds) (2007) Computational methods of feature selection. CRC Press, Boca Raton
Hall MA (1999) Correlation-based feature selection for machine learning. Doctoral dissertation, The University of Waikato
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: Jaafar A, Ali NM, Noah SAM, Smeaton AF, Bruza P, Bakar ZA, Jamil N, Sembok TMT (eds) Information retrieval technology. Springer International Publishing, Berlin, pp 482–493
Purvis MK, Kasabov N, Benwell G, Zhou Q, Zhang F (1998) Neuro-fuzzy methods for environmental modelling. Department of Information Science, University of Otago, Otago
Heath M, Bowyer K, Kopans D, Moore R, Kegelmeyer WP (2000) The digital database for screening mammography. In: Proceedings of the 5th international workshop on digital mammography. Medical Physics Publishing, pp 212–218
American College of Radiology. BI-RADS Committee (1998) Breast imaging reporting and data system. American College of Radiology (Ed.). American College of Radiology
Subasini A, Abubacker NF (2014) Analysis of classifier to improve Medical diagnosis for Breast Cancer Detection using Data Mining Techniques. Int J Adv Netw Appl 5(6):2117
Petrosian A, Chan HP, Helvie MA, Goodsitt MM, Adler DD (1994) Computer-aided diagnosis in mammography: classification of mass and normal tissue by texture analysis. Phys Med Biol 39(12):2273
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
Tai SC, Chen ZS, Tsai WT (2014) An automatic mass detection system in mammograms based on complex texture features. IEEE J Biomed Health Inf 18(2):618–627
Parekh R (2010) Using texture analysis for medical diagnosis. IEEE Multimedia 2:28–37
Wang X, Georganas ND, Petriu EM (2011) Fabric texture analysis using computer vision techniques. IEEE Trans Instrum Meas 60(1):44–56
Park S, Kim B, Lee J, Goo JM, Shin YG (2011) GGO nodule volume-preserving nonrigid lung registration using GLCM texture analysis. IEEE Trans Biomed Eng 58(10):2885–2894
Grim J, Somol P, Haindl M, Daneš J (2009) Computer-aided evaluation of screening mammograms based on local texture models. IEEE Trans Image Process 18(4):765–773
Gao X, Wang Y, Li X, Tao D (2010) On combining morphological component analysis and concentric morphology model for mammographic mass detection. IEEE Trans Inf Technol Biomed 14(2):266–273
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
Veldkamp WJ, Karssemeijer N, Otten JD, Hendriks JH (2000) Automated classification of clustered microcalcifications into malignant and benign types. Med Phys 27(11):2600–2608
Abu-Amara F, Abdel-Qader I (2009) Hybrid mammogram classification using rough set and fuzzy classifier. J Biomed Imaging 2009:17
Viton JL, Rasigni M, Rasigni G, Llebaria A (1996) Method for characterizing masses in digital mammograms. Opt Eng 35(12):3453–3459
Lisboa PJ (2002) A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw 15(1):11–39
de OliveiraMartins L, Junior GB, Silva AC, de Paiva AC, Gattass M (2009) Detection of masses in digital mammograms using K-means and support vector machine. ELCVIA Electron Lett Comput Vis Image Anal 8(2):39–50
Bovis K, Singh S (2002) Classification of mammographic breast density using a combined classifier paradigm. In Medical image understanding and analysis (MIUA) conference, Portsmouth
Raso G, Magro R, Fauci F (2004) Mammogram segmentation by contour searching and massive lesion classification with neural network. In: IEEE nuclear science symposium conference record
Eddaoudi F, Regragui F, Mahmoudi A, Lamouri N (2011) Masses detection using SVM classifier based on textures analysis. Appl Math Sci 5(8):367–379
Singh BK (2011) Mammographic image enhancement, classification and retrieval using color, statistical and spectral Analysis. Int J Comput Appl 10:18–23
Mavroforakis ME, Georgiou HV, Dimitropoulos N, Cavouras D, Theodoridis S (2006) Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers. Artif Intell Med 37(2):145–162
Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. ACM SIGMOD Record 22(2):207–216
Antonie ML, Zaiane OR, Coman A (2002) Associative classifiers for medical images. In: Zaïane OR, Simoff SJ, Djeraba C (eds) Mining multimedia and complex data. Springer, Berlin, pp 68–83
Ribeiro MX, Traina AJ, Balan AG, Traina C, Marques P (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
Tseng VS, Wang MH, Su JH (2005) A new method for image classification by using multilevel association rules. In: 21st international conference on data engineering workshops, 2005. IEEE, pp 1180–1180
Yun J, Zhanhuai L, Yong W, Longbo Z (2005) Joining associative classifier for medical images. In: Fifth international conference on hybrid intelligent systems, 2005. HIS’05. IEEE, p 6
Ribeiro MX, Traina C, Azevedo-Marques PM (2008) An association rule-based method to support medical image diagnosis with efficiency. IEEE Trans Multimedia 10(2):277–285
Watanabe CY, Ribeiro MX, Traina Jr C, Traina AJ (2010) SACMiner: a new classification method based on statistical association rules to mine medical images. In: Filipe J, Cordeiro J (eds) Enterprise information systems. Springer, Berlin, pp 249–263
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), vol 1. IEEE, pp 564–567
Park J, Sandberg IW (1991) Universal approximation using radial-basis-function networks. Neural Comput 3(2):246–257
Platt J (1998) Sequential minimal optimization: a fast algorithm for training support vector machines. Technical Report MSR-TR-98-14
Rish I (2001) An empirical study of the naive Bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, vol 3, No. 22. IBM, New York, pp 41–46
Holte RC (1993) Very simple classification rules perform well on most commonly used datasets. Mach Learn 11(1):63–90
Salzberg SL (1994) C4. 5: Programs for machine learning by j. ross quinlan. morgan Kaufmann Publishers, Inc., 1993. Mach Learn 16(3):235–240
Ma BLWHY (1998) Integrating classification and association rule mining. In: Proceedings of the fourth international conference on knowledge discovery and data mining
Li W, Han J, Pei J (2001) CMAR: accurate and efficient classification based on multiple class-association rules. In: Proceedings IEEE international conference on data mining, 2001. ICDM 2001. IEEE, pp 369–376
Yin X, Han J (2003) CPAR: classification based on predictive association rules. In: SDM, vol 3. pp 331–335
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Abubacker, N.F., Azman, A., Doraisamy, S. et al. An integrated method of associative classification and neuro-fuzzy approach for effective mammographic classification. Neural Comput & Applic 28, 3967–3980 (2017). https://doi.org/10.1007/s00521-016-2290-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-016-2290-z