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...

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Main Authors: Suhartono, Nguyen, P.T., Shankar, K., Hashim, W., Maseleno, A.
Format: Article
Language:English
Published: 2020
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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
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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