SpringerLink
Log in

Breast Cancer Detection Using Hybrid Segmentation Using FOA and FCM Clustering

  • Conference paper
  • First Online:
Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science (ICMMCS 2023)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1450))

Abstract

Medical image processing has recently been widely applied in a variety of fields. Finding the anomaly problems in that image is highly beneficial for the early diagnosis of these ailments. There are several techniques available for segmenting MRI images to find breast cancer. Breast cancer is the second greatest cause of death in women. Early detection of breast cancer reduces the number of women who die from cancer. If caught in time, breast cancer is among the forms of cancer that can be cured. In this study, breast cancer is identified in medical photos using a unique hybrid segmentation technique. Fruitfy optimization technique (FOA) and FCM clustering are both used in hybrid segmentation. To get a more accurate value of the clustering centers in FCM Clustering, a Fruitfy optimization algorithm (FOA) approach was applied. The MRI images’ features are extracted using the Extended Gabor wavelet transform (IGWT). When compared to other approaches, the result demonstrates that the hybrids segment performs with great performance and good accuracy of 96.50%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Melekoodappattu, J. G., Subbian, P. S., & Queen, M. F. (2021). Detection and classification of breast cancer from digital mammograms using hybrid extreme learning machine classifier. International Journal of Imaging Systems and Technology, 31(2), 909–920.

    Article  Google Scholar 

  2. Huang, H., Feng, X. A., Zhou, S., Jiang, J., Chen, H., Li, Y., & Li, C. (2019). A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features. BMC Bioinformatics, 20(8), 1–14.

    Google Scholar 

  3. Prakash, R. M., Bhuvaneshwari, K., Divya, M., Sri, K. J., & Begum, A. S. (2017). Segmentation of thermal infrared breast images using K-means, FCM, and EM algorithms for breast cancer detection. In 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1–4). IEEE.

    Google Scholar 

  4. Kannan, S. R., Ramathilagam, S., Devi, R., & Sathya, A. (2011). Robust kernel FCM in segmentation of breast medical images. Expert Systems with Applications, 38(4), 4382–4389.

    Article  Google Scholar 

  5. Hassanien, A. E., Moftah, H. M., Azar, A. T., & 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.

    Article  Google Scholar 

  6. Melekoodappattu, J. G., & Subbian, P. S. (2020). Automated breast cancer detection using hybrid extreme learning machine classifier. Journal of Ambient Intelligence and Humanized Computing, 1–10.

    Google Scholar 

  7. Kavitha, P., & Prabakaran, S. (2019). A novel hybrid segmentation method with particle swarm optimization and fuzzy c-mean based on partitioning the image for detecting lung cancer.

    Google Scholar 

  8. Krishnakumar, S., & Manivannan, K. (2021). Effective segmentation and classification of brain tumor using rough K mean algorithm and multi-kernel SVM in MR images. Journal of Ambient Intelligence and Humanized Computing, 12(6), 6751–6760.

    Article  Google Scholar 

  9. Al-Ayyoub, M., AlZu’bi, S. M., Jararweh, Y., & Alsmirat, M. A. (2016). A GPU-based breast cancer detection system using single pass fuzzy c-means clustering algorithm. In 2016 5th International Conference on Multimedia Computing and Systems (ICMCS) (pp. 650–654). IEEE.

    Google Scholar 

  10. Shen, L., Chen, H., Yu, Z., Kang, W., Zhang, B., Li, H., ... & Liu, D. (2016). Evolving support vector machines using fruit fly optimization for medical data classification. Knowledge-Based Systems, 96, 61–75

    Google Scholar 

  11. JerlinRubini, L., & Perumal, E. (2020). Efficient classification of chronic kidney disease by using multi-kernel support vector machine and fruit fly optimization algorithm. International Journal of Imaging Systems and Technology, 30(3), 660–673.

    Article  Google Scholar 

  12. Kapila, D., & Bhagat, N. (2022). Efficient feature selection technique for brain tumor classification utilizing hybrid fruit fly-based ABC and ANN algorithm. Materials Today: Proceedings, 51, 12–20.

    Google Scholar 

  13. Cahoon, T. C., Sutton, M. A., & Bezdek, J. C. (2000). Breast cancer detection using image processing techniques. In Ninth IEEE International Conference on Fuzzy Systems. FUZZ-IEEE 2000 (Cat. No. 00CH37063) (Vol. 2, pp. 973–976). IEEE.

    Google Scholar 

  14. Zhang, X., Zhang, C., Tang, W., & Wei, Z. (2012). Medical image segmentation using improved FCM. Science China Information Sciences, 55(5), 1052–1061.

    Article  MathSciNet  Google Scholar 

  15. Harrabi, R., & Braiek, E. B. (2014). Color image segmentation using a modified Fuzzy C-Means technique and different color spaces: Application in the breast cancer cells images. In 2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP) (pp. 231–236). IEEE.

    Google Scholar 

  16. Singh, N., Mohapatra, A. G., & Kanungo, G. (2011). Breast cancer mass detection in mammograms using K-means and fuzzy C-means clustering. International Journal of Computer Applications, 22(2), 15–21.

    Article  Google Scholar 

  17. Kanungo, G. K., Singh, N., Dash, J., & Mishra, A. (2015). Mammogram image segmentation using hybridization of fuzzy clustering and optimization algorithms. In Intelligent Computing, Communication and Devices (pp. 403–413). Springer, New Delhi.

    Google Scholar 

  18. Singh, D., Sahana, S., Pal, S., Nath, I., Bhattacharyya, S. (2020). Assessment of the heart disease using soft computing methodology. In: Solanki, V., Hoang, M., Lu, Z., Pattnaik, P. (Eds.), Intelligent Computing in Engineering. Advances in Intelligent Systems and Computing, vol 1125. Springer, Singapore. https://doi.org/10.1007/978-981-15-2780-7_1.

  19. Suseendran, G., Chandrasekaran, E., Pal, S., Elangovan, V. R., & Nagarathinam, T. (2021). Comparison of multidimensional hyperspectral image with SIFT image mosaic methods for mosaic better accuracy. In Intelligent Computing and Innovation on Data Science: Proceedings of ICTIDS 2021 (pp. 201–212). Springer Singapore. https://doi.org/10.1007/978-981-16-3153-5_23.

  20. Suseendran, G., Balaganesh, D., Akila, D., & Pal, S. (2021). Deep learning frequent pattern mining on static semi structured data streams for improving fast speed and complex data streams. In 2021 7th International Conference on Optimization and Applications (ICOA) (pp. 1–8). IEEE. https://doi.org/10.1109/ICOA51614.2021.9442621.

  21. Jeyalaksshmi, S., Akila, D., Padmapriya, D., Suseendran, G., & Pal, S. (2021). Human facial expression based video retrieval with query video using EBCOT and MLP. In Proceedings of First International Conference on Mathematical Modeling and Computational Science: ICMMCS 2020 (pp. 157–166). Springer Singapore.https://doi.org/10.1007/978-981-33-4389-4_16

  22. Suseendran, G., Doss, S., Pal, S., Dey, N., & Quang Cuong, T. (2021). An approach on data visualization and data mining with regression analysis. In Proceedings of First International Conference on Mathematical Modeling and Computational Science: ICMMCS 2020 (pp. 649–660). Springer Singapore. https://doi.org/10.1007/978-981-33-4389-4_59

  23. Pal, S., Suseendran, G., Akila, D., Jayakarthik, R., & Jabeen, T. N. (2021). Advanced FFT architecture based on Cordic method for Brain signal Encryption system. In 2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM) (pp. 92–96). IEEE.doi: https://doi.org/10.1109/ICCAKM50778.2021.9357770

  24. Rakshit, P., Nath, I., & Pal, S. (2020). Application of IoT in healthcare. Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm, pp. 263–277. https://doi.org/10.1007/978-3-030-33596-0_10

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Sister Nivedita University, Kolkata, India

    Souvik Pal & Saikat Maity

  2. School of Engineering, Swami Vivekananda University, Kolkata, India

    Saurabh Adhikari

  3. Collage of Engineering, Medical Instruments Technology Engineering, National University of Science and Technology, Dhi Qar, Iraq

    Mohammed Ayad Alkhafaji

  4. Department of Computer Science, University of Oviedo, Oviedo, Spain

    Vicente García Díaz

Authors
  1. Souvik Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saikat Maity
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saurabh Adhikari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammed Ayad Alkhafaji
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vicente García Díaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Souvik Pal .

Editor information

Editors and Affiliations

  1. Department Of Creative Technologies and Product Design, National Taipei University of Business, Taoyuan, Taiwan

    Sheng-Lung Peng

  2. School of Computer Science, SCS, Taylor's University, Subang Jaya, Malaysia

    Noor Zaman Jhanjhi

  3. Department of Computer Science and Engineering, Sister Nivedita University, Kolkata, West Bengal, India

    Souvik Pal

  4. College of Engineering and Computer Science, Joshi Research Center 489, Wright State University, Dayton, OH, USA

    Fathi Amsaad

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pal, S., Maity, S., Adhikari, S., Alkhafaji, M.A., Díaz, V.G. (2023). Breast Cancer Detection Using Hybrid Segmentation Using FOA and FCM Clustering. In: Peng, SL., Jhanjhi, N.Z., Pal, S., Amsaad, F. (eds) Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science. ICMMCS 2023. Advances in Intelligent Systems and Computing, vol 1450. Springer, Singapore. https://doi.org/10.1007/978-981-99-3611-3_6

Download citation

Publish with us

Policies and ethics

Search

Navigation