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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my.uniten.dspace-245352023-05-29T15:24:19Z Brain tumor segmentation and classification using KNN algorithm Suhartono Nguyen P.T. Shankar K. Hashim W. Maseleno A. 57210948011 57216386109 56884031900 11440260100 55354910900 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. Final 2023-05-29T07:24:18Z 2023-05-29T07:24:18Z 2019 Article 10.35940/ijeat.F1137.0886S19 2-s2.0-85071991938 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071991938&doi=10.35940%2fijeat.F1137.0886S19&partnerID=40&md5=0ebe337cd92e3cb84d6e3e74821356c6 https://irepository.uniten.edu.my/handle/123456789/24535 8 6 Special Issue 706 711 All Open Access, Bronze Blue Eyes Intelligence Engineering and Sciences Publication Scopus |
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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. |
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57210948011 |
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57210948011 Suhartono Nguyen P.T. Shankar K. Hashim W. Maseleno A. |
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Suhartono Nguyen P.T. Shankar K. Hashim W. Maseleno A. |
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Suhartono Nguyen P.T. Shankar K. Hashim W. Maseleno A. Brain tumor segmentation and classification using KNN algorithm |
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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 |
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brain tumor segmentation and classification using knn algorithm |
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Blue Eyes Intelligence Engineering and Sciences Publication |
publishDate |
2023 |
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