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Enhancing the Efficiency of Parallel Genetic Algorithms for Medical Image Processing with Hadoop

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 108 - Number 17
Year of Publication: 2014
Authors:
D. Peter Augustine
10.5120/19002-0483

Peter D Augustine. Article: Enhancing the Efficiency of Parallel Genetic Algorithms for Medical Image Processing with Hadoop. International Journal of Computer Applications 108(17):11-16, December 2014. Full text available. BibTeX

@article{key:article,
	author = {D. Peter Augustine},
	title = {Article: Enhancing the Efficiency of Parallel Genetic Algorithms for Medical Image Processing with Hadoop},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {108},
	number = {17},
	pages = {11-16},
	month = {December},
	note = {Full text available}
}

Abstract

In this paper, there is in-depth analysis of the parallel genetic algorithms used for segmentation of brain images and how their efficiency varies in the cloud setup with Hadoop. Since the current health care industry is moving towards the utmost usage of cloud to make the data available round the clock for the analysis, it is mandatory that the efficiency of the analysis also to be enhanced to produce the accurate result. Here, the focus is on the study of medical image processing that too narrowed down to the brain images with the help of parallel genetic algorithms in the cloud environment. The study aims to help the researchers to augment the competence of the algorithm when it functions in the remote cloud setup.

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