Breast Cancer Detection Using Supervised Machine Learning Algorithm
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.
.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.
.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.
.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.
.Melanie A. Sutton, “Breast Cancer Detection Using Image Processing Techniques”, IEEE International Conference on Fuzzy System Febraury 2000.
.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.
.“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.
.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.
.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.
.Elter, M. Horsch, A. CADx of mammographic masses and clustered micro classifications: A review. Med. Phys. 36, 2052–2068, 2009.
.Fenton, J. J. et al. Influence of Computer-Aided Detection on Performance of Screening Mammography. New Engl. J. Medicine 356, 1399–1409 2007.
.Cole, E. B. et al. Impact of Computer-Aided Detection Systems on Radiologist Accuracy With Digital Mammography. Am. J. Roentgenol. 203, 909–916 2014.
.Lehman, C. D. et al. Diagnostic Accuracy of Digital Screening Mammography With and Without Computer- Aided Detection. JAMA Intern. Medicine 175, 1828– 1837, 2015.
.LeCun, Y., Bengio, Y. Hinton, G. Deep learning. Nature, volume 521, pp 436–444 , 2015.
.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.
.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.
.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.
.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
.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.
.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
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
To ensure uniformity of treatment among all contributors, other forms may not be substituted for this form, nor may any wording of the form be changed. This form is intended for original material submitted to AJCT and must accompany any such material in order to be published by AJCT. Please read the form carefully.
The undersigned hereby assigns to the Asian Journal of Convergence in Technology Issues ("AJCT") all rights under copyright that may exist in and to the above Work, any revised or expanded derivative works submitted to AJCT by the undersigned based on the Work, and any associated written, audio and/or visual presentations or other enhancements accompanying the Work. The undersigned hereby warrants that the Work is original and that he/she is the author of the Work; to the extent the Work incorporates text passages, figures, data or other material from the works of others, the undersigned has obtained any necessary permission. See Retained Rights, below.
AJCT distributes its technical publications throughout the world and wants to ensure that the material submitted to its publications is properly available to the readership of those publications. Authors must ensure that The Work is their own and is original. It is the responsibility of the authors, not AJCT, to determine whether disclosure of their material requires the prior consent of other parties and, if so, to obtain it.
RETAINED RIGHTS/TERMS AND CONDITIONS
1. Authors/employers retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
2. Authors/employers may reproduce or authorize others to reproduce The Work and for the author's personal use or for company or organizational use, provided that the source and any AJCT copyright notice are indicated, the copies are not used in any way that implies AJCT endorsement of a product or service of any employer, and the copies themselves are not offered for sale.
3. Authors/employers may make limited distribution of all or portions of the Work prior to publication if they inform AJCT in advance of the nature and extent of such limited distribution.
4. For all uses not covered by items 2 and 3, authors/employers must request permission from AJCT.
5. Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
INFORMATION FOR AUTHORS
AJCT Copyright Ownership
It is the formal policy of AJCT to own the copyrights to all copyrightable material in its technical publications and to the individual contributions contained therein, in order to protect the interests of AJCT, its authors and their employers, and, at the same time, to facilitate the appropriate re-use of this material by others.
If you are employed and prepared the Work on a subject within the scope of your employment, the copyright in the Work belongs to your employer as a work-for-hire. In that case, AJCT assumes that when you sign this Form, you are authorized to do so by your employer and that your employer has consented to the transfer of copyright, to the representation and warranty of publication rights, and to all other terms and conditions of this Form. If such authorization and consent has not been given to you, an authorized representative of your employer should sign this Form as the Author.
AJCT requires that the consent of the first-named author and employer be sought as a condition to granting reprint or republication rights to others or for permitting use of a Work for promotion or marketing purposes.
1. The undersigned represents that he/she has the power and authority to make and execute this assignment.
2. The undersigned agrees to indemnify and hold harmless AJCT from any damage or expense that may arise in the event of a breach of any of the warranties set forth above.
3. In the event the above work is accepted and published by AJCT and consequently withdrawn by the author(s), the foregoing copyright transfer shall become null and void and all materials embodying the Work submitted to AJCT will be destroyed.
4. For jointly authored Works, all joint authors should sign, or one of the authors should sign as authorized agent
for the others.
Licenced by :