Clustered ensemble neural network for breast mass classification in digital mammography

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2012, Pages

  Mc Leod, Peter ^a   Verma, Brijesh ^a  

^a CENTRAL QUEENSLAND UNIVERSITY   (Australia)

Author keywords

classifier; clustering; digital mammograms; neural network ensemble

Indexed keywords

BENCHMARK DATABASE; BREAST MASS; CLASSIFICATION ACCURACY; CLUSTERING; DIGITAL MAMMOGRAMS; DIGITAL MAMMOGRAPHY; ENSEMBLE NEURAL NETWORK; EXISTING SYSTEMS; K-MEANS; MULTI LAYER PERCEPTRON; NEURAL NETWORK ENSEMBLES;

CLASSIFIERS; MAMMOGRAPHY; NETWORK LAYERS; X RAY SCREENS;

NEURAL NETWORKS;

EID: 84865076207     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2012.6252539     Document Type: Conference Paper

References (22)

1. D. Styllanos, M. Mavroforakis, H. Georgiou, N. Dimitropoulos and S. Theodoridis, "A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry," Computer Methods and Programs in Biomedicine, 2011, April 2011, vol. 102, no. 1, pp. 47-63.
2. B. Verma and P. Zhang, "A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms," Applied Soft Computing, vol. 7, no. 2, 2007, pp. 612-625.
3. B. Verma, "Novel network architecture and learning algorithm for the classification of mass abnormalities in digitized mammograms," Artificial Intelligence in Medicine, vol. 42, no. 1, 2008, pp. 67-79.
4. J. Dheeba and S. Tamil, "Screening Mammogram images for Abnormalities using Radial Basis Function Neural Network," Communication Control and Computing Technologies (ICCCT), 2010 IEEE International Conference, pp. 554-559, doi:10.1109/ICCCT.2010.5670778.
5. rd International Conference on Computer Science and Information Technology (ICCSIT'10), pp. 115-119, Chengdu, doi:10.1109/ICCSIT.2010.5564054.
6. R. Brem, "Clinical versus research approach to breast cancer detection with CAD: where are we now?", American Journal of Roentology, vol. 188, 2007, pp. 234-235.
7. th International Conference on Informatics and Systems (INFOS'10), Cairo, pp. 1-7.
8. R. Nishikawa, M. Kallergi, "Computer-aided detection, in its present form, is not an effective aid for screening mammography", Medical Physics, vol. 33, 2006, pp. 811-814.
9. A. Malich, S. Schmidt, D. Fischer, M. Facius, W. Kaiser, "The performance of computer-aided detection when analysing prior mammograms of newly detected breast cancers with special focus on the time interval from initial imaging to detection", European Journal of Radiology, 2008, doi:10.1016/j.ejrad.2007.11.038.
10. th Mediterranean Conference on Control and Automation, Marakesh, Morocco, 2010, pp. 1308-1315.
11. D. Roder, N. Houssami, G. Farshid, G. Gill, P. Downey, K. Beckmann, P. Iosifidis, L. Grieve and L. Williamson, "Population screening and intensity of screening are associated with reduced breast cancer mortality: evidence of efficacy of mammography screening in Australia", Breast Cancer Research and Treatment, vol. 108, 2008, pp. 409-416.
12. B. Surendiran and A. Vadivel, "Feature Selection using Stepwise ANOVA Discriminant Analysis for Mammogram Mass Classification," International Journal of Recent Trends in Engineering and Technology, vol. 3, no. 2, pp. 55-57, doi:01.IJRTET.03.02.195.
13. M. Heath, K. Bowyer, D. Kopans, R. Moore and P. Kegelmeyer, "The Digital Database for Screening Mammography," 2001, IWDM-2000, Medical Physics Publishing.
14. L. Vanovcanova, V. Lehotska and K. Rauova, "Digital Mammography a new trend in carcinoma diagnosis," Bratislava Medical Journal, 2010, vol. 111, no. 9, pp. 510-513.
15. L. Kuncheva, "Combining pattern classifiers: methods and algorithms", 2004, Wiley-IEEE Press, New York.
16. J. Rodriguez, L. Kuncheva and C. Alonso, "Rotation forest: a new classifier ensemble method", Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2006, vol. 28, no. 10, pp. 1619-1630.
17. Y. Zhang, N. Tomuro, J. Furst and D. Raicu, "Building an ensemble system for diagnosing masses in mammograms", International Journal of Computer Assisted Radiology and Surgery (CARS), 2011, June 2011, doi: 10.1007/s11548-011-0628-7.
18. S. Luo and B. Cheng, "Diagnosing Breast Masses in Digital Mammography Using Feature Selection and Ensemble Methods", 2010, Journal of Medical Systems, May 2010, doi: 10.1007/s10916-010-9518-8.
19. A. Rahman and B. Verma, "A Novel Layered Clustering based Approach for Generating Ensemble of Classifiers", Neural Networks, IEEE Transactions on, 2011, vol. 22, no. 5 (May 2011), pp. 781-792. doi: 10.1109/TNN.2011. 2118765.
20. M.P. Sampat, A.C. Bovik and M.K. Markey, "Classification of mammographic lesions into BI-RADS Shape Categories using the Beamlet Transform," In proceedigns of the SPIE, Medical Imaging: Image Processing 2005, pp. 16-25.
21. L. I. Kuncheva, C.J. Whitaker, C.A. Shipp and R.P.W. Duin, "Limits on the Majority Vote Accuracy in Classifier Fusion," Pattern Analysis and Applications, 2003, vol. 6, pp. 22-31.
22. S. Omar, Z. Saad, M.K. Osman, I. Isa and J.M. Saleh, "Improved Classification Performance for Multiple Multilayer Perceptron (MMLP) Network using Voting Technique," in proceedings of Fourth Asia International Conference on Mathematics/Analytical Modelling and Computer Simulation, 2010, pp. 247-252.