Determining the Malignancy of Breast Cancer Using a Combined Neural Network-Support Vector Machine Scheme

Bahareh Hemmati, Hosein Jafarkarimi

Abstract


Data mining has been useful in medical diagnosis area. The current research proposes a new combined classification technique based on support vector machine and neural network to determine malignancy of breast cancer tumours. The proposed method uses a meta-heuristic algorithm to find the best weights for combining the results of two aforementioned techniques. The numerical and statistical results confirm the superiority of our model in comparison to the previous methods. Following the results, the best accuracies are 98.23% and 96.85% for proposed and the previous methods, respectively. The method has potential to be implemented as a decision support system for breast cancer diagnosis.


Keywords


Classification, Breast cancer, Artificial Neural Network, Support Vector Machine, Wilcoxon hypothesis test

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Abstract

References


Addeh, J., & Ebrahimzadeh, A. (2012). Breast cancer recognition using a novel hybrid intelligent method. medical signals and sensors, 2 (2), 95-102.

Arevalo, J., González, F., Ramos-Pollán, R., Oliveira, J., & Lopez, M. (2016). Representation learning for mammography mass lesion classification with convolutional neural networks. Computer methods and programs in biomedicine, 127, 248-257.

Chen, S. T., Hsiao, Y. H., Huang, Y. L., Kuo, S. J., Tseng, H. S., Wu, H. K., & Chen, D. R. (2009). Comparative Analysis of Logistic Regression, Support Vector Machine and Artificial Neural Network for the Differential Diagnosis of Benign and Malignant Solid Breast Tumors by the Use of Three-Dimensional Power Doppler Imaging. Korean Journal of Radiology, 10, 464-471.

de Sampaio, W., Silva, A., de Paiva, A., & Gattass, M. (2015). Detection of masses in mammograms with adaption to breast density using genetic algorithm, phylogenetic trees, LBP and SVM. Expert Systems with Applications, 42 (22), 8911-8928.

Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques.

Hassanien, A., Moftah, H., Azar, A., & Shoman, M. (2014). MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier. Applied Soft Computing, 14, 62-71.

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

Kalteh, A., Zarbakhsh, P., Jirabadi, M., & Addeh, J. (2013). A research about breast cancer detection using different neural networks and K-MICA algorithm. Journal of cancer research and therapeutics, 9 (3), 456-466.

Korkmaz, S., & Poyraz, M. (2015). Least Square Support Vector Machine and Minimum Redundancy Maximum Relevance for Diagnosis of Breast Cancer from Breast Microscopic Images. Social and Behavioral Sciences, 174, 4026-4031.

Lisboa, P., Bourdes, V., Bonevay, S., Defrance, R., Perol, D., & Chabaud, S. (2010). Comparison of artificial neural network with logistic regression as classification models for variable selection for prediction of breast cancer patient outcomes. Artificial Neural Systems, 1, 12-39.

Liu, H., Zhang, R., Luan, F., Yao, X., Liu, M., Hu, Z., & Fan, B. (2003). Diagnosing breast cancer based on support vector machines. Chemical Information and Computer Sciences, 43 (3), 900-907.

Majid, A., Ali, S., Iqbal, M., & Kausar, N. (2014). Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines. Computer methods and programs in biomedicine, 113 (3), 792-808.

Marcano-Cedeño, A., Quintanilla-Domínguez, J., & Andina, D. (2011 ). WBCD breast cancer database classification applying artificial metaplasticity neural network. Expert Systems with Applications, 38 (8), 9573-9579.

McCulloch, W., & Pitts, W. (1943). A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, 5 (4), 115-133.

McLaren, C., Chen, W., Nie, K., & Su, M. (2009). Prediction of malignant breast lesions from MRI features: a comparison of artificial neural network and logistic regression techniques. Academic radiology, 16 (7), 842-851.

Mishra, G., Ananth, V., Shelke, K., Sehgal, D., & Valadi, J. (2015). Hybrid ACO Chaos-Assisted Support Vector Machines for Classification of Medical Datasets. Paper presented at the InProceedings of Fourth International Conference on Soft Computing for Problem Solving .

Naushada, S.M., Ramaiaha, J., Pavithrakumaria, M.,Jayapriyaa, J., Hussainb, T., Alrokayanc, S.A., Gottumukkalad, S.R., Digumartie, R., & Kutalaf, V.K. (2016). Artificial neural network-based exploration of gene-nutrient interactions in folate and xenobiotic metabolic pathways that modulate susceptibility to breast cancer. Gene, 580 (2): p. 159-168.

Polat, K., & Günes, S. (2007). Breast cancer diagnosis using least square support vector machine. Digital Signal Processing, 17, 694-701.

Quinlan, J. R. (1996). Improved use of continuous attributes in C4.5. Artificial Intelligence Research, 4, 77-90.

Rouhi, R., & Jafari, M. (2016). Classification of benign and malignant breast tumors based on hybrid level set segmentation. Expert Systems with Applications, 46, 45-59.

Rouhi, R., Jafari, M., Kasaei, S., & Keshavarzian, P. (2015). Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Systems with Applications, 42 (3), 990-1002.

Salama, G., Abdelhalim, M. B., & Zeid, M. (2012). Breast Cancer diagnosis on three different datasets using multi-classifiers. International Journal of Computer and Information Technology, 1, 36-43.

Sattlecker, M., Baker, R., Stone, N., & Bessant, C. (2011). Support vector machine ensembles for breast cancer type prediction from mid-FTIR micro-calcification spectra. Chemometrics and Intelligent Laboratory Systems, 107 (2), 363-370.

Sivakami, K. (2015). Mining Big Data: Breast Cancer Prediction using DT-SVM Hybrid Model. International Journal of Scientific Engineering and Applied Science (IJSEAS), 1 (5).

Song, J. H., Venkatesh, S., Conant, E. A., Arger, P. H., & Sehgal, C. M. (2005). Comparative Analysis of Logistic Regression and Artificial Neural Network for Computer-Aided Diagnosis of Breast Masses. Academic Radiology, 12, 487-495.

Stewart, B., & Wild, C. (2014). World cancer report Retrieved from World Health Organization

Stoean, R., & Stoean, C. (2013). Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection. Expert Systems with Applications, 40 (7), 2677-2686.

Sweilam, N., Tharwat, A., & Moniem, N. (2010 ). Support vector machine for diagnosis cancer disease: A comparative study. Egyptian Informatics Journal, 11 (2), 81-92.

Tu, J. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. clinical epidemiology, 49 (11), 1225-1231.

Utomo, C., Kardiana, A., & Yuliwulandari, R. (2014). Breast cancer diagnosis using artificial neural networks with extreme learning techniques. Advanced Research in Artificial Intelligence, 3 (7).

Uzer, M., Inan, O., & Yılmaz, N. (2013). A hybrid breast cancer detection system via neural network and feature selection based on SBS, SFS and PCA. Neural Computing and Applications, 23 (3-4), 719-728.

Vapnik, V. (1998). Statistical Learning Theory. Wiley,New York.

Wang, D., Shi, L., & Heng, P. (2009). Automatic detection of breast cancers in mammograms using structured support vector machines. Neurocomputing, 72 (13), 3296-3302.

Wang, P., Hu, X., Li, Y., Liu, Q., & Zhu, X. (2016). Automatic cell nuclei segmentation and classification of breast cancer histopathology images. Signal Processing, 31 (122), 1-3.

Wisconsin Breast Cancer Dataset (WBDC) (2014). (Original)..

Zheng, B., Yoon, S., & Lam, S. (2014). Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Systems with Applications, 41 (4), 1476-1482.


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