Breast Cancer Classification Using Deep Neural Networks

  • S. Karthik
  • R. Srinivasa Perumal
  • P. V. S. S. R. Chandra Mouli
Chapter

Abstract

Early diagnosis of any disease can be curable with a little amount of human effort. Most of the people fail to detect their disease before it becomes chronic. It leads to increase in death rate around the world. Breast cancer is one of the diseases that could be cured when the disease identified at earlier stages before it is spreading across all the parts of the body. The medical practitioner may diagnose the diseases mistakenly due to misinterpretation. The computer-aided diagnosis (CAD) is an automated assistance for practitioners that will produce accurate results to analyze the criticality of the diseases. This chapter presents a CAD system to perform automated diagnosis for breast cancer. This method employed deep neural network (DNN) as classifier model and recursive feature elimination (RFE) for feature selection. DNN with multiple layers of processing attained higher classification rate than SVM. So, the researchers used deep learning method for hyper-spectral data classification. This chapter used DNN to learn deep features of data. The DNN with multiple layers of processing is applied to classify the breast cancer data. The system was experimented on Wisconsin Breast Cancer Dataset (WBCD) from UCI repository. The dataset partitioned into different sets of train-test split. The performance of the system is measured based on accuracy, sensitivity, specificity, precision, and recall. From the results, the accuracy obtained 98.62%, which is better than other state-of-the-art methods. The results show that the system is comparatively outperformed than the existing system.

Keywords

Classification Deep learning Healthcare system Feature selection Breast cancer diagnosis 

References

  1. 1.
    Abbass, H. A. (2002). An evolutionary artificial neural networks approach for breast cancer diagnosis. Artificial Intelligence in Medicine, 25(3), 265–281.CrossRefGoogle Scholar
  2. 2.
    Abdel-Zaher, A. M., & Eldeib, A. M. (2016). Breast cancer classification using deep belief networks. Expert Systems with Applications, 46, 139–144.CrossRefGoogle Scholar
  3. 3.
    Anooj, P. (2012). Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules. Journal of King Saud University-Computer and Information Sciences, 24(1), 27–40.CrossRefGoogle Scholar
  4. 4.
    Azar, A. T., & El-Said, S. A. (2014). Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Computing and Applications, 24(5), 1163–1177.CrossRefGoogle Scholar
  5. 5.
    Bewal, R., Ghosh, A., & Chaudhary, A. (2015). Journal of Clinical and Biomedical Sciences, 5(4), 143–148.Google Scholar
  6. 6.
    Bhattacherjee, A., Roy, S., Paul, S., Roy, P., Kausar, N. & Dey, N. (2015). Classification approach for breast cancer detection using back propagation neural network: a study. Biomedical Image Analysis and Mining Techniques for Improved Health Outcomes, p. 210.Google Scholar
  7. 7.
    Ghosh, S., Biswas, S., Sarkar, D. C., & Sarkar, P. P. (2016). Breast cancer detection using a neuro-fuzzy based classification method. Indian Journal of Science and Technology, 9(14).Google Scholar
  8. 8.
    Gorunescu, F., & Belciug, S. (2014). Evolutionary strategy to develop learning-based decision systems. Application to breast cancer and liver fibrosis stadialization. Journal of Biomedical Informatics, 49, 112–118.CrossRefGoogle Scholar
  9. 9.
    Huang, M. W., Chen, C. W., Lin, W. C., Ke, S. W., & Tsai, C. F. (2017). Svm and svm ensembles in breast cancer prediction. PloS one, 12(1), e0161501.Google Scholar
  10. 10.
    Jhajharia, S., Varshney, H. K., Verma, S., & Kumar, R. (2016). A neural network based breast cancer prognosis model with pca processed features. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1896–1901.Google Scholar
  11. 11.
    Jouni, H., Issa, M., Harb, A., Jacquemod, G., & Leduc, Y. (2016). Neural network architecture for breast cancer detection and classification. In: IEEE International Multidisciplinary Conference on Engineering Technology (IMCET), pp. 37–41.Google Scholar
  12. 12.
    Karabatak, M., & Ince, M. C. (2009). An expert system for detection of breast cancer based on association rules and neural network. Expert Systems with Applications, 36(2), 3465–3469.CrossRefGoogle Scholar
  13. 13.
    Kiyan, T., & Yildirim, T. (2004). Breast cancer diagnosis using statistical neural networks. Journal of Electrical and Electronics Engineering, 4(2), 1149–1153.Google Scholar
  14. 14.
    Mert, A., Kılıc¸, N., Bilgili, E., & Akan, A. (2015). Breast cancer detection with reduced feature set. Computational and Mathematical Methods in Medicine.Google Scholar
  15. 15.
    Nahato, K. B., Harichandran, K. N., & Arputharaj, K. (2015). Knowledge mining from clinical datasets using rough sets and backpropagation neural network. Computational and Mathematical Methods in Medicine.Google Scholar
  16. 16.
    Nayak, T., Dash, T., Rao, D. C., & Sahu, P. K. (2016). Evolutionary neural networks versus adaptive resonance theory net for breast cancer diagnosis. In: Proceedings of the International Conference on Informatics and Analytics (ACM), p. 97.Google Scholar
  17. 17.
    Nilashi, M., Ibrahim, O., Ahmadi, H., & Shahmoradi, L. (2017). A knowledge-based system for breast cancer classification using fuzzy logic method. Telematics and Informatics, 34(4), 133–144.CrossRefGoogle Scholar
  18. 18.
    Onan, A. (2015). A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer. Expert Systems with Applications, 42(20), 6844–6852.CrossRefGoogle Scholar
  19. 19.
    Paulin, F., & Santhakumaran, A. (2011). Classification of breast cancer by comparing back propagation training algorithms. International Journal on Computer Science and Engineering, 3(1), 327–332.Google Scholar
  20. 20.
    Prevention Control: Center for Diseases Control and Prevention (2014). URL https://www.cdc.gov/cancer/breast/index.htm.
  21. 21.
    Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.CrossRefGoogle Scholar
  22. 22.
    Yin, Z., Fei, Z., Yang, C., & Chen, A. (2016). A novel svm-rfe based biomedical data processing approach: Basic and beyond. In: IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, pp. 7143–7148.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • S. Karthik
    • 1
  • R. Srinivasa Perumal
    • 1
  • P. V. S. S. R. Chandra Mouli
    • 2
  1. 1.School of Information Technology and EngineeringVIT UniversityVelloreIndia
  2. 2.School of Computer Science and EngineeringVIT UniversityVelloreIndia

Personalised recommendations