Breast Cancer Detection Using Supervised Machine Learning Algorithm

  • Shashidhar R
  • Arunakumari B N
  • Naziya Farheen H S
  • Puneeth S B
  • Santhosh Kumar R
  • Roopa M

Abstract

The most commonly causing cancer among Indian women is breast cancer and it effecting all over world with its impact. According to the medical reports of breast cancer patients in India were unable to hold the pain and about half of them are dying. In the proposed work used a machine learning algorithm to decrease the pre-processing time and to detection the symptoms and for better accuracy. The system is trained pre-processed image of fed to the system which are in the form of mammograms in common the X-ray of breast. The system which has the data segregated into the training and testing datasets analyses the input images based on the characters or the labels assigned to them done with the application of few of the algorithms which are present in the machine learning we compare the data or the image and probable output based on the character labels is obtained in the form of result. Compared to existing work and the proposed machine learning model as a serious of combination and permutations of algorithms lead to increase in the efficacy of the result and got the accuracy of 97.4% using random forests algorithm.

Keywords: Breast cancer, machine learning, X-ray, random forests algorithm.

Downloads

Download data is not yet available.

References

[1].M.M.Mehdy, E.E.Shair and P.Y.Ng, “Artificial Neural Networks in Image Processing for Earlier Detection of Breast Cancer”, Hindawi, Computational and Mathematical Methods in Medicine, Volume 2017, Article ID 2610628.
[2].Vishnukumar K.Patel, Prof.Syed Uvaid and Prof.A.C.Suthar, “Mammogram of Breast Cancer Detection Based Using Image Enhancement Algorithm”, Internationa Journal of Engineering Technology and Advanced Engineering, Volume 2, Issue 8, August 2012.
[3].Moh’d Rasoul A Al-Hadidi, Mohammed Y. Al- Gawagzeh, “Solving Mammography Problems of Breast Cancer Detection Using Artificial Neural Networks and Image Processing Techniques”, Indian Journal of Science and Technology, Vol 5, No.4 (April 2012), ISSN: 094-6846.
[4].Bhagyashri k Yadav, Dr. Prof. M. S. Panse, “Virtual Instrumentation Based Breast Cancer Detection and Classification Using Image- Processing”, International Journal of Research and Scientific Innovation (IJRSI), Volume V, Issue IV, April 2018.
[5].Melanie A. Sutton, “Breast Cancer Detection Using Image Processing Techniques”, IEEE International Conference on Fuzzy System Febraury 2000.
[6].A. D. Belsare and M. M. Mushrif, Histopathology Image Analysis Using Image Processing Technique, Signal & Image Processing : An International Journal (SIPIJ) Vol.3, No.4, August 2012.
[7].“Latest Global Cancer Data: Cancer burden rises to 18.1 million new cases and 9.6 million cancer deaths in 2018”, International Agency for Research on Cancer, World Health Organization, 12 September 2018.
[8].Oeffinger, K. C. et al. Breast Cancer Screening for Women at Average Risk: 2015 Guideline Update From the American Cancer Society. JAMA 314, 1599–1614,2015.
[9].Lehman, C. D. et al. National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium. Radiol. 283, 49–58, 2016.
[10].Elter, M. Horsch, A. CADx of mammographic masses and clustered micro classifications: A review. Med. Phys. 36, 2052–2068, 2009.
[11].Fenton, J. J. et al. Influence of Computer-Aided Detection on Performance of Screening Mammography. New Engl. J. Medicine 356, 1399–1409 2007.
[12].Cole, E. B. et al. Impact of Computer-Aided Detection Systems on Radiologist Accuracy With Digital Mammography. Am. J. Roentgenol. 203, 909–916 2014.
[13].Lehman, C. D. et al. Diagnostic Accuracy of Digital Screening Mammography With and Without Computer- Aided Detection. JAMA Intern. Medicine 175, 1828– 1837, 2015.
[14].LeCun, Y., Bengio, Y. Hinton, G. Deep learning. Nature, volume 521, pp 436–444 , 2015.
[15].Aboutalib SS, Mohamed AA, Berg WA, Zuley ML, Sumkin JH, Wu S. Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening. Clin Cancer Res. 2018;24(23):5902- 5909. doi:10.1158/1078-0432.CCR-18-1115.
[16].Kim, E., Kim, H., Han, K. et al. Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study. Sci Rep 8, 2762 (2018). https://doi.org/10.1038/s41598-018- 21215-1.
[17].Hamidinekoo A, Denton E, Rampun A, Honnor K, Zwiggelaar R. Deep learning in mammography and breast histology, an overview and future trends. Med Image Anal.2018;47:45-67. doi:10.1016/j.media. 2018.03.006.
[18].Burt JR, Torosdagli N, Khosravan N, et al. Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks. Br J Radiol.2018;91(1089):20170545. doi:10.1259/bjr.20170545
[19].Kooi T, Litjens G, van Ginneken B, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal. 2017;35:303- 312. doi:10.1016/j.media.2016.07.007.
[20].Shreekanth T., Shashidhar R. (2018) An Application of Image Processing Technique for Compression of ECG Signals Based on Region of Interest Strategy. In: Hemanth D., Smys S. (eds) Computational Vision and Bio Inspired Computing. Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-71767-8_85
Comments

Statistics
0 Views | 0 Downloads
How to Cite
R, S., B N, A., H S, N. F., S B, P., R, S. K., & M, R. (2020). Breast Cancer Detection Using Supervised Machine Learning Algorithm. Asian Journal For Convergence In Technology (AJCT), 6(3), 26-31. https://doi.org/10.33130/AJCT.2020v06i03.006