Breast Cancer Detection using Machine Learning and Image Processing

K. Maheswari*, Tadipathri Sayed Afreen**
*-** Department of Electronics and Communication Engineering, Sanskrithi School of Engineering, Puttaparthi, Andhra Pradesh, India.
Periodicity:January - June'2022
DOI : https://doi.org/10.26634/jpr.9.1.18805

Abstract

Nowadays breast cancer is the frequent type of cancer in women which leads to death. Mammography and ultrasound are the common ways to detect breast cancer. This paper employs Machine Learning for identification of breast cancer using mammography images. Ultrasound and Elastography are the combined imaging techniques used to separate benign and malignant breast lesions. Support vector machine is a classifier which is used for the classification of combined B-mode and Elastography image. This paper helps the physician to detect breast cancer earlier.

Keywords

Breast Cancer, Elastography, Image Processing, B-mode (Ultra Sound), SVM.

How to Cite this Article?

Maheswari, K., and Afreen, T. S. (2022). Breast Cancer Detection using Machine Learning and Image Processing. i-manager’s Journal on Pattern Recognition, 9(1), 8-14. https://doi.org/10.26634/jpr.9.1.18805

References

[1]. Indian Cancer Society. (2018). Retrieved from https:// www.indiancancersociety.org/pdf/ics-report-18-19.pdf
[2]. Padmanabhan, S., & Sundararajan, R. (2012). Enhanced accuracy of breast cancer detection in digital mammograms using wavelet analysis. In 2012 International Conference on Machine Vision and Image Processing (MVIP), 153-156. https://doi.org/10.1109/MVIP.2012.6428783
[3]. Spandana, P., & Rao, K. M., Rao, B. V. V. S. N. P., & Jwalasrikala. (2013). Novel image processing techniques for early detection of breast cancer, mat lab and lab view implementation. In 2013 IEEE Point-of-Care Healthcare Technologies (PHT), 105-108. https://doi.org/10.1109/PHT.2013.6461295
[4]. Alto, H., Rangayyan, R. M., & Desautels, J. L. (2005). Content-based retrieval and analysis of mammographic masses. Journal of Electronic Imaging, 14(2). https://doi.org/10.1117/1.1902996
[5]. Tao, Y., Lo, S. C. B., Freedman, M. T., & Xuan, J. (2007). A preliminary study of content-based mammographic masses retrieval. In Medical Imaging 2007: Computer- Aided Diagnosis, 6514, 624-635. https://doi.org/10.1117/12.711528
[6]. Zheng, B., Lu, A., Hardesty, L. A., Sumkin, J. H., Hakim, C. M., Ganott, M. A., & Gur, D. (2006). A method to improve visual similarity of breast masses for an interactive computer aided diagnosis environment. Medical Physics, 33(1), 111-117. https://doi.org/10.1118/1.2143139
[7]. Wei, C. H., Li, Y., & Huang, P. J. (2011). Mammogram retrieval through machine learning within BI-RADS standards. Journal of Biomedical Informatics, 44(4), 607-614. https://doi.org/10.1016/j.jbi.2011.01.012
[8]. Narvaez, F., Díaz, G., & Romero, E. (2011). Multi-view information fusion for automatic BI-RADS description of mammographic masses. In Medical Imaging 2011: Computer-Aided Diagnosis. 7963, 81-87.
[9]. Liu, J., Zhang, S., Liu, W., Zhang, X., & Metaxas, D. N. (2014). Scalable mammogram retrieval using anchor graph hashing. In 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 898-901.
[10]. Liu,W., Wang, J.,Kumar S., & ChangS. (2011). Hashing with graphs. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), 1–8.
[11]. Guo, H., & Nandi, A. K. (2006). Breast cancer diagnosis using genetic programming generated feature. Pattern Recognition, 39(5), 980-987. https://doi.org/10.1016/j.patcog.2005.10.001
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Online 15 15

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.