Brain tumor segmentation and classification using KNN algorithm
Image processing plays a vital role in MRI image processing. MRI images are widely used in medical fields for analysis and detection of tumour growth from the body. There are varieties of efficient brain tumour detection and segmentation methods have been suggested by various researchers for efficie...
Saved in:
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
2020
|
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.uniten.dspace-12904 |
---|---|
record_format |
dspace |
spelling |
my.uniten.dspace-129042020-07-07T04:23:58Z Brain tumor segmentation and classification using KNN algorithm Suhartono Nguyen, P.T. Shankar, K. Hashim, W. Maseleno, A. Image processing plays a vital role in MRI image processing. MRI images are widely used in medical fields for analysis and detection of tumour growth from the body. There are varieties of efficient brain tumour detection and segmentation methods have been suggested by various researchers for efficient tumour detection. Existing methods encounter with several challenges such as detection time, accuracy and quality of tumour. In this review paper, we are presenting a study of various tumour detection methods for MRI images. A comparative analysis has been also performed for various methods.SAR images are the high resolution images which cannot be collected manually. In this work, we identified the SAR images randomly from web with different region inclusions. The regions in an image include water area, land area and the mountain area. The implementation of proposed model is done in MATLAB environment. © BEIESP. 2020-02-03T03:27:42Z 2020-02-03T03:27:42Z 2019 Article 10.35940/ijeat.F1137.0886S19 en |
institution |
Universiti Tenaga Nasional |
building |
UNITEN Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Tenaga Nasional |
content_source |
UNITEN Institutional Repository |
url_provider |
http://dspace.uniten.edu.my/ |
language |
English |
description |
Image processing plays a vital role in MRI image processing. MRI images are widely used in medical fields for analysis and detection of tumour growth from the body. There are varieties of efficient brain tumour detection and segmentation methods have been suggested by various researchers for efficient tumour detection. Existing methods encounter with several challenges such as detection time, accuracy and quality of tumour. In this review paper, we are presenting a study of various tumour detection methods for MRI images. A comparative analysis has been also performed for various methods.SAR images are the high resolution images which cannot be collected manually. In this work, we identified the SAR images randomly from web with different region inclusions. The regions in an image include water area, land area and the mountain area. The implementation of proposed model is done in MATLAB environment. © BEIESP. |
format |
Article |
author |
Suhartono Nguyen, P.T. Shankar, K. Hashim, W. Maseleno, A. |
spellingShingle |
Suhartono Nguyen, P.T. Shankar, K. Hashim, W. Maseleno, A. Brain tumor segmentation and classification using KNN algorithm |
author_facet |
Suhartono Nguyen, P.T. Shankar, K. Hashim, W. Maseleno, A. |
author_sort |
Suhartono |
title |
Brain tumor segmentation and classification using KNN algorithm |
title_short |
Brain tumor segmentation and classification using KNN algorithm |
title_full |
Brain tumor segmentation and classification using KNN algorithm |
title_fullStr |
Brain tumor segmentation and classification using KNN algorithm |
title_full_unstemmed |
Brain tumor segmentation and classification using KNN algorithm |
title_sort |
brain tumor segmentation and classification using knn algorithm |
publishDate |
2020 |
_version_ |
1672614189044072448 |
score |
13.214268 |