A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

Authors

  • J. Magelin Mary  Assistant Professor, Holy Cross College, Trichy, Tamil Nadu, India
  • K. Chitra  Assistant Professor, Holy Cross College, Trichy, Tamil Nadu, India
  • Y. Arockia Suganthi  Assistant Professor, Holy Cross College, Trichy, Tamil Nadu, India

DOI:

https://doi.org//10.32628/CSEIT183863

Keywords:

Digital mammography, Ant colony optimization, Genetic algorithm, mammography, Image pre-processing.

Abstract

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-12-30

Issue

Volume 3, Issue 8, November-December-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
J. Magelin Mary, K. Chitra, Y. Arockia Suganthi, " A Comparative Study of Ant and Genetic Algorithms in Digital Mammography , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 8, pp.194-200, November-December-2018. Available at doi : https://doi.org/10.32628/CSEIT183863