Cascade generalization for breast cancer detection

Mammography is known as the preferred method for breast cancer diagnosis. Researchers have proposed machine learning based methods to improve the detection of breast cancer using mammography. In this study, cascade generalization is proposed for breast cancer detection. Four Bayesian Network based methods, SVM, and C4.5 are evaluated in loose coupled cascade classifier. The Bayesian based methods are evaluated in both base level and meta level. The evaluation results show the superiority of the proposed cascade strategy compared to Bagging and single classifier approach. Naive Bayes with SMO cascade demonstrated the best result in terms of ROC area under curve of 0.903. Bayesian Network using Tabu search with SMO cascade demonstrated the best accuracy of 83.689%.

[1]  Nahla Barakat,et al.  Cascade generalization: Is SVMs' inductive bias useful? , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[2]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[3]  Ismail Saritas,et al.  Prediction of Breast Cancer Using Artificial Neural Networks , 2012, Journal of Medical Systems.

[4]  R. Bouckaert.  Bayesian belief networks : from construction to inference , 1995 .

[5]  Jasmina Novakovic,et al.  Interpretation of mammograms with rotation forest and PCA , 2011, 2011 6th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI).

[6]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.

[7]  J. Platt.  Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[8]  Bor-Wen Cheng,et al.  Diagnosing Breast Masses in Digital Mammography Using Feature Selection and Ensemble Methods , 2012, Journal of Medical Systems.

[9]  João Gama,et al.  Cascade Generalization , 2000, Machine Learning.

[10]  S. Kotsiantis,et al.  Cascade Generalization with Classification and Model Trees , 2008, 2008 Third International Conference on Convergence and Hybrid Information Technology.

[11]  Maryellen L. Giger,et al.  Computer-aided diagnosis of breast lesions in medical images , 2000, Comput. Sci. Eng..

[12]  J. Goo,et al.  SRA 626 : Statistical Analysis , 2004 .

[13]  Aytürk Keles,et al.  Expert system based on neuro-fuzzy rules for diagnosis breast cancer , 2011, Expert Syst. Appl..

[14]  Nir Friedman,et al.  Bayesian Network Classifiers , 1997, Machine Learning.

[15]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[16]  M. Elter,et al.  The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process. , 2007, Medical physics.

[17]  Alaa M. Elsayad,et al.  2 . Material and Methods 2 . 1 Mammographic Masses Data Set , 2013 .