Development of breast cancer diagnosis system based on fuzzy logic and probabilistic neural network

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.202820

Keywords:

breast cancer diagnosis, fuzzy logic (FL), probabilistic neural network (PNN)

Abstract

Breast cancer is one of the most common kinds of cancers that infect females in the whole world. It has happened when the cells in breast tissues start to grow in an uncontrollable way. Because it leads to death, early detection and diagnosis is a very important task to save the patient's life. Due to the restriction of human observers, computer plays a significant role in detecting early cancer signs. The proposed system uses a multi-resolution analysis and a top-hat operation for detecting the suspicious regions in a mammogram image. The discrete wavelet transform feature analysis is utilized for extracting features from the region of interest. Fuzzy Logic (FL) and Probabilistic Neural Network (PNN) are utilized for classifying the tumor into normal or abnormal. The differences between the proposed system and other researches are the use of adaptive threshold value depending on each image, by using Discrete Wavelet Transform (DWT) in both segmentation and feature extraction phases, which decrease complexity and time. Additionally, the detection of more than one tumor in the breast mammogram image and the utilization of FL and PNN work on increasing the system efficiency that led to raising the accuracy rate of the system and reducing the time. The obtained results of accuracy, sensitivity, and specificity were equal to 99 %, 98 %, and 47 %, respectively, and these results showed that the proposed system is more accurate than the other previous related works

Supporting Agencies

  • Department of Computer Science
  • College of Science
  • University of Diyala
  • Iraq

Author Biographies

Taha Mohammed Hasan, University of Diyala Diyala, Iraq

PhD of Computer Science, Assistant Professor, Head of Department

Department of Computer Science, College of Science

Sahab Dheyaa Mohammed, University of Information Technology and Communications Baghdad, Iraq

PhD of Computer Science-Security, Lecturer

Jumana Waleed, University of Diyala Diyala, Iraq

PhD of Computer Science, Assistant Professor

Department of Computer Science, College of Science

References

  1. Narain Ponraj, M. E. J. D., Poongodi, P., Samuel Manoharan, J. (2011). A Survey on the Preprocessing Techniques of Mammogram For the Detection of Breast Cancer. Journal of Emerging Trends in Computing and Information Sciences (ISSN), 2, 656–664.
  2. Kahya, M. A. (2019). Classification enhancement of breast cancer histopathological image using penalized logistic regression. Indonesian Journal of Electrical Engineering and Computer Science, 13 (1), 405. doi: https://doi.org/10.11591/ijeecs.v13.i1.pp405-410
  3. Abdullah, A. J., Hasan, T. M., Waleed, J. (2019). An Expanded Vision of Breast Cancer Diagnosis Approaches Based on Machine Learning Techniques. 2019 International Engineering Conference (IEC). doi: https://doi.org/10.1109/iec47844.2019.8950530
  4. Bhardwaj, A., Tiwari, A., Chandarana, D., Babel, D. (2014). A genetically optimized neural network for classification of breast cancer disease. 2014 7th International Conference on Biomedical Engineering and Informatics. doi: https://doi.org/10.1109/bmei.2014.7002862
  5. Saini, S., Vijay, R. (2015). Mammogram Analysis Using Feed-Forward Back Propagation and Cascade-Forward Back Propagation Artificial Neural Network. 2015 Fifth International Conference on Communication Systems and Network Technologies. doi: https://doi.org/10.1109/csnt.2015.78
  6. Naranje, S. (2016). Early Detection of Breast Cancer using ANN. International Journal of Innovative Research in Computer and Communication Engineering, 4 (7), 14008–14013.
  7. Tan, Y. J., Sim, K. S., Ting, F. F. (2017). Breast cancer detection using convolutional neural networks for mammogram imaging system. 2017 International Conference on Robotics, Automation and Sciences (ICORAS). doi: https://doi.org/10.1109/icoras.2017.8308076
  8. Routray, I., Rath, N. P. (2018). Textural Feature Based Classification of Mammogram Images Using ANN. 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT). doi: https://doi.org/10.1109/icccnt.2018.8493957
  9. Liu, S., Zeng, J., Gong, H., Yang, H., Zhai, J., Cao, Y. et. al. (2018). Quantitative analysis of breast cancer diagnosis using a probabilistic modelling approach. Computers in Biology and Medicine, 92, 168–175. doi: https://doi.org/10.1016/j.compbiomed.2017.11.014
  10. Feng, H., Cao, J., Wang, H., Xie, Y., Yang, D., Feng, J., Chen, B. (2020). A knowledge-driven feature learning and integration method for breast cancer diagnosis on multi-sequence MRI. Magnetic Resonance Imaging, 69, 40–48. doi: https://doi.org/10.1016/j.mri.2020.03.001
  11. Specht, D. F. (1990). Probabilistic neural networks and the polynomial Adaline as complementary techniques for classification. IEEE Transactions on Neural Networks, 1 (1), 111–121. doi: https://doi.org/10.1109/72.80210
  12. Specht, D. F. (1992). Enhancements to probabilistic neural networks. [Proceedings 1992] IJCNN International Joint Conference on Neural Networks, 1, 761–768. doi: https://doi.org/10.1109/ijcnn.1992.287095
  13. Kusy, M., Zajdel, R. (2014). Probabilistic neural network training procedure based on Q(0)-learning algorithm in medical data classification. Applied Intelligence, 41 (3), 837–854. doi: https://doi.org/10.1007/s10489-014-0562-9
  14. Sawant, S. S., Topannavar, P. S. (2015). Introduction to Probabilistic Neural Network–Used for Image Classifications. International Journal of Advanced Research in Computer Science and Software Engineering, 5 (4), 279–283.

Downloads

Published

2020-08-31

How to Cite

Mohammed Hasan, T., Dheyaa Mohammed, S., & Waleed, J. (2020). Development of breast cancer diagnosis system based on fuzzy logic and probabilistic neural network. Eastern-European Journal of Enterprise Technologies, 4(9 (106), 6–13. https://doi.org/10.15587/1729-4061.2020.202820

Issue

Section

Information and controlling system