A Review on Hybrid Bio Inspired PSO-ACO Technique Using KNN for Detection of Brain Tumor

Authors(2) :-Er.Anita Devi, Er.Jagdeep Kaur

Magnetic Resonance Imaging scan produce detailed study of the internal structure of human brain and other parts of the body. Brain tumor segmentation from brain MRI image is a very remarkable task. A large number of techniques have been proposed for the automatic brain tumor detection and segmentation from the brain MRI images and scans. In this paper, the existing brain tumor detection and segmentation techniques for brain MRI images have been discussed for the Comparative study of particle swarm optimization and ant colony optimization based clustering techniques for detection of brain tumor in MRI images. The brain tumor segmentation are detected for the KNN techniques. The Brain tumor segmentation are the different tissues for active cells and edema from normal brain tissues of White Matter, Gray Matter.MRI based brain tumor segmentation studies are attraction and attention in latest years due to non-invasive imaging and good soft tissue contrast of Magnetic Resonance Imaging (MRI) images.

Authors and Affiliations

Er.Anita Devi
Research Scholar, Department of CSE, SBBSUIET, Padhiana, Punjab, India
Er.Jagdeep Kaur
Assistant Professor, Department of CSE, SBBSUIET, Padhiana, Punjab, India

Brain MRI Images, Clustering, PSO, ACO, KNN, Tumor Segmentation

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Publication Details

Published in : Volume 3 | Issue 7 | September-October 2018
Date of Publication : 2018-10-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 228-235
Manuscript Number : CSEIT1836140
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Er.Anita Devi, Er.Jagdeep Kaur, "A Review on Hybrid Bio Inspired PSO-ACO Technique Using KNN for Detection of Brain Tumor ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 7, pp.228-235, September-October-2018.
Journal URL : http://ijsrcseit.com/CSEIT1836140

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