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Detection of breast tumor in medical images using modified neural network model

Author Affiliations

  • 1MATS School of IT, MATS University, Raipur, CG, India
  • 2MATS School of IT, MATS University, Raipur, CG, India

Res. J. Computer & IT Sci., Volume 7, Issue (2), Pages 6-11, December,20 (2019)

Abstract

It is well versed that “Bioinformatics” is multi disciplinary area for research. It has two conditions biology information and computer science. Computer technology used on biological problems that have various challenges to computer information as identifying images processing, Structured to unstructured, data and analyze, health care, data like as X-ray, MRI, DNA, Breast Cancer, Diabetes. In bio Analysis various special fields of computer information technology can be valid for its knowledge of Machine Learning (Deep Learning), Data mining, image processing, data visualization, Data Reduction, 2-D and 3-D construction. In image processing Breast Screening is a technique to use mammographic images for detects cancerous tumors in the breast whether it is normal. We have developed our model in two parts first model named MBPNN as machine learning architecture to identify breast tumor. In second part cancerous tumor is cropped and processed. At last we have compared our parametric values with other models such as SVM and Distance classifier models. This model is developed using Mat Lab programming. In this model 300 images have been tested. This model identifies the location of tumor and tells us the stage of tumor. All parameters such as minimum, variance, dispersion and many more is presented.

References

  1. Report (2017)., American Cancer Society Cancer.org., 1.800.227.2345 September 21 2017 (www.cancer.org/cancer/acs-medical-content-and-news-staff.html)
  2. Narain B. and Sharma U. (2019)., Impact of Breast Cancer in women′s of Satna District., Madhya Pradesh, 7(3), ISSN 2347-2693.
  3. Tomar R.S., Singh T., Wadhwani S. and Bhadoria S.S. (2009)., Analysis of breast cancer using image processing techniques., 2009 Third UKSim European Symposium on Computer Modeling and Simulation, 251-256. IEEE.
  4. Sharma Usha (2017)., Suitability of Neural Network for disease prediction a comprehensive literature., 5(6), 15-20. ISSN 2320-6527.
  5. Cancer image (2019) https://www.google.com/ search?client=firefox-b d&tbm=isch&q=cancerous+ breast+tissues&chips=q:difference+between+normal+tumor+and+cancer+tumor+in+breast,online_chips:malignant&usg=A, undefined, undefined
  6. Gustavo ferrero (2006)., Detection of Breast Lesions in Medical Digital Imaging Using Neural Networks., 218, 1-18.
  7. Deshmukh J. and Bhosle U. (2016)., Image mining using association rule for medical image dataset., Procedia Computer Science, 85, 117-124.
  8. Jamge S.B. and Charate A.P. (2017)., The Preprocessing Methods of Mammogram Images for Breast Cancer Detection., IJRTTCC, 5(1), 261-264, ISSN 2321-8169
  9. Gonzalez R.C., Woods R.E. and Eddins S.L. (2009)., Digital image processing using Matlab., Gatesmark Publishing. ISBN 9780131687288.
  10. Pratap R. (2010)., Getting started with MATLAB: a quick introduction for scientists and engineers., USA: Oxford University Press. ISBN 9780199731244.