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Efficient Early Detection of Breast Cancer Detection using ANN and DWT

Parvati N. Angadi, M. Nagendra. Published in Image Processing.

Communications on Applied Electronics
Year of Publication: 2018
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Parvati N. Angadi, M. Nagendra
10.5120/cae2018652745

Parvati N Angadi and M Nagendra. Efficient Early Detection of Breast Cancer Detection using ANN and DWT. Communications on Applied Electronics 7(13):1-6, February 2018. BibTeX

@article{10.5120/cae2018652745,
	author = {Parvati N. Angadi and M. Nagendra},
	title = {Efficient Early Detection of Breast Cancer Detection using ANN and DWT},
	journal = {Communications on Applied Electronics},
	issue_date = {February 2018},
	volume = {7},
	number = {13},
	month = {Feb},
	year = {2018},
	issn = {2394-4714},
	pages = {1-6},
	numpages = {6},
	url = {http://www.caeaccess.org/archives/volume7/number13/797-2018652745},
	doi = {10.5120/cae2018652745},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Excavating the prior literature shows that there has been an abundant prior studies in the area of breast cancer detection but, very little work has been put towards 'Early Detection of Breast Cancer.' In the country like US, where majority of the women has higher vulnerabilities of becoming a victim of breast cancer, as evident from history, early detection of breast cancer can play a boon in medical science. This paper therefore makes an attempt where the system is designed considering a dataset of mammogram from DDSM where feature extraction is performed using Discrete Wavelet Transform (DWT), and the feature vectors are then efficiently trained by Artificial Neural Network (ANN). The final trained results are stored in matrix and validation is performed using real time mammogram image to exhibit that the proposed model has successfully accomplished more than 90% in accuracy, sensitivity, and specificity.

References

  1. http://www.kdheks.gov/edw/ks_women_facts.html
  2. https://www.clinicalkey.com/topics/surgery/breast-cancer.html
  3. R.N. Strickland, Image-Processing Techniques for Tumor Detection, Marcel Dekker, 2002
  4. P-L. Chang and W-G. Teng, Exploiting the Self-Organizing Map for Medical Image Segmentation, Twentieth IEEE International Symposium on Computer-Based Medical Systems, 2007
  5. G. M. Brake and N. Karssemeijer, Single and Multiscale Detection of Masses in Digital Mammograms, IEEE Transactions on Medical Imaging, Vol. 18, No. 7, July 1999
  6. H. Qil, P.T. Kuruganti, Z. Lid, Early Detection of Breast Cancer using Thermal Texture Maps, IEEE, 2002
  7. Y. Xie, B. Guo, J. Li, P. Stoica, On multi-static adaptive microwave imaging methods for early breast cancer detection, IEEE, 2006
  8. Y. Xie, B. Guo, L. Xu, J.Li, P. Stoica, Multistatic Adaptive Microwave Imaging for Early Breast Cancer Detection, IEEE Transactions On Biomedical Engineering, Vol. 53, No. 8, August 2006
  9. X. Xiao and T. Kikkawa, Extraction of Calibration Waveform for Confocal Microwave Imaging for Early Breast Cancer Detection, International Symposium on Microwave, Antenna, Propagation, and EMC Technologies For Wireless Communications, IEEE, 2007
  10. X. Xiao, X. Liu, B.Liu, Study on Microwave Imaging for the Early Breast Cancer Detection by FDTD with PML Boundary Condition, International Conference on Future BioMedical Information Engineering, IEEE, 2009
  11. X. Xiao, T. Kikkawa, Early Breast Cancer Detection with Hemi-elliptical Configuration by UWB Imaging, IEEE, 2009
  12. Y. Ireaneus, A.Rejani, S.Thamarai Selvi, Early detection of breast cancer Using SVM classifier technique, International Journal on Computer Science and Engineering Vol.1(3), 2009, 127-130
  13. A.M. Abbosh, Early Breast Cancer Detection Using Doppler Frequency Shift, Proceedings of Asia-Pacific Microwave Conference, 2010
  14. M. Halloran, E. Jones, M. Glavin, Quasi-Multistatic MIST Beamforming for the Early Detection of Breast Cancer, IEEE Transactions On Biomedical Engineering, Vol. 57, No. 4, April 2010
  15. B. C. Patel, G.R.Sinha, Early Detection of Breast Cancer using Self Similar Fractal Method, International Journal of Computer Applications (0975 – 8887), Volume 10– N.4, November 2010
  16. A. Mencattini, M. Salmeri, P. Casti, Bilateral asymmetry identification for the early detection of breast cancer, IEEE, 2011
  17. T-I., Chiu, T-C., Hsiao, Shi-Bing, Luo, WanTing Tien, A feasibility study of the optoacoustic imaging of microcalcification for early breast cancer detection, IEEE 2011
  18. M. Guardiola, S. Capdevila, J.Romeu, L. Jofre, 3-D Microwave Magnitude Combined Tomography for Breast Cancer Detection Using Realistic Breast Models, IEEE antennas and wireless propagation letters, Vol. 11, 2012
  19. P. Spandana, K.M.M. Rao, Novel Image Processing Techniques for Early Detection of Breast Cancer, Matlab and Lab view implementation, IEEE Point-of-Care Healthcare Technologies, 2013
  20. C-H., Wei, Y. Li, C-T. Li, Effective extraction of Gabor features for adaptive mammogram retrieval, IEEE, 2007
  21. K. N. Batmanghelich, D.H., Ye, K.M. Pohl, B. Taskar, C. Davatzikos, and Adni, Disease classification and prediction via semi-supervised dimensionality reduction, IEEE, 2011
  22. Sivandam SN, Deepa SN (2007). Principles of Soft Computing, Wiley India, pp. 11-29.
  23. http://marathon.csee.usf.edu/Mammography/Database.html

Keywords

Breast Cancer Detection, Artificial Neural Network, Discrete Wavelet Transfor, DDSM