Carcinoma Detection using Convolution Neural Networks

Authors

  • Rajiv Gopal  Asst. Professor, Dept. of ECE New Horizon College of Engineering, Bengaluru, India
  • Naveen H  Asst. Professor, Dept. of ECE New Horizon College of Engineering, Bengaluru, India
  • Karthik C V  Asst. Professor, Dept. of ECE New Horizon College of Engineering, Bengaluru, India

Keywords:

Convolution Neural Networks, Flattening, ReLU

Abstract

In this paper, we have proposed an optimized model which can predict the symptoms of breast cancer with an accuracy of 86%. The machine learned to predict test images at a more accurate rate than it could without optimization. Using Random forest, we got an accuracy of 83%. We have used Convolutional Neural Network to develop a model for breast cancer detection through a mammograph dataset. With the rapid development in deep learning, in the future, machine learning will surely bring much improvement in development of models for prediction, detection of several health issues even at an early stage and easier procedure. We have used Python language for the implementation of entire system.

References

  1. A. Alarabeyyat, A.M., "Breast Cancer Detection Using K-Nearest Neighbor Machine Learning Algorithm" in 9th International Conference on, IEEE, v.i.e.E.(DeSE), pp. 35-39, 2016.
  2. A. Francillon, P.R., "Smart Card Research and Advanced Applications" in 12th International Conference CARDIS 2013, Berlin, Germany:Springer International Publishing.
  3. S.K. Prabhakar, H. Rajaguru, N. Maglaveras, I. Chouvarda, P. de Carvalho, "Performance Analysis of Breast Cancer Classification with Softmax Discriminant Classifier and Linear Discriminant Analysis" in Precision Medicine Powered by pHealth and Connected Health. IFMBE Proceedings, Singapore, Springer, vol. 66, 2018.
  4. L.A. Altonen, R. Saalovra, P. Kristo, F. Canzian, A. Hemminki, P Peltomaki, R. Chadwik, A. De La Chapelle, "Incidence of hereditary nonpolyposis colorectal cancer and the feasibility of molecular screening for the disease", N Engl J Med, vol. 337, pp. 1481-1487, 1998.
  5. S. Chakraborty, "Bayesian kernel probit model for microarray based cancer classification", Computational Statistics and Data Analysis, vol. 12, pp. 4198-4209, 2009.
  6. I. Guyon, J. Weston, S. Barnhill, V. Vapnik, "Gene selection for cancer classification using support vector machines", Machine Learning, vol. 46, pp. 389-422, 2002.
  7. S. Gokhale, "Ultrasound characterization of breast masses", The Indian journal of radiology &imaging, vol. 19, pp. 242-249, 2009.
  8. T. Jinshan, R.R., X. Jun, I. El Naqa, Y. Yongyi, "Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances" in Information Technology in Biomedicine, IEEE, vol. 13, pp. 236-251, 2009.
  9. A. Jemal, R.S., E. Ward, Y. Hao, J. Xu, T. Murray, M.J. Thun, "Cancer statistics", A Cancer Journal for Clinicians, vol. 58, pp. 71- 96, 2008.
  10. L. Adi Tarca, V.J.C., X. Chen, R. Romero, S. Drăghici, "Machine Learning and Its Applications to Biology", PLoS Comput Biol, vol. 3, pp. 116-122, 2007.
  11. JF McCarthy, M.K., PE Hoffman, "Applications of machine learning and high-dimensional visualization in cancer detection diagnosis and management", Ann N Y Acad Sci, vol. 62, pp. 10201259, 2004.
  12. AC. Tan, D. Gilbert, "Ensemble machine learning on gene expression data for cancer classification", Appl. Bioinform, vol. 2, pp. 75-83, 2003.
  13. S. Kanta Sarkar, A.N., "Identifying patients at risk of breast cancer through decision trees", International Journal of Advanced Research in Computer Science, vol. 08, pp. 88-96, 2017.
  14. JA. Cruz, W.D, "Applications of Machine Learning in Cancer Prediction and Prognosis", Cancer Inform, vol. 2, pp. 56-77, 2006.
  15. M. Sugiyama, Introduction to Statistical Machine Learning, T. Green: Morgan Kaufmann, 2006.
  16. A. Lavecchia, "machine-learning approaches in the context of ligand- based virtual screening for addressing complex compound classification problems and predicting new active molecules", D. Montesano, vol. 49, 2005.
  17. P. Baldi, S.R.B., S.r.B. Pierre Baldi, Bioinformatics: The machine learning approach, 2001.
  18. N. Bhatia, "Survey of Nearest Neighbor Techniques", International Journal of Computer Science and Information Security, vol. 8, no. 2, 2010.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2019-12-30

Issue

Volume 4, Issue 9, November-December-2019

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Rajiv Gopal, Naveen H, Karthik C V, " Carcinoma Detection using Convolution Neural Networks" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 9, pp.536-541, November-December-2019.