Brain tumor segmentation and classification using KNN algorithm

Suhartono, Phong Thanh Nguyen, K. Shankar, Wahidah Hashim, Andino Maseleno

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)706-711
Number of pages6
JournalInternational Journal of Engineering and Advanced Technology
Volume8
Issue number6 Special Issue
DOIs
Publication statusPublished - 01 Aug 2019

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Tumors
Brain
Magnetic resonance imaging
Image processing
Image resolution
MATLAB
Water

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Engineering(all)
  • Computer Science Applications

Cite this

Suhartono ; Nguyen, Phong Thanh ; Shankar, K. ; Hashim, Wahidah ; Maseleno, Andino. / Brain tumor segmentation and classification using KNN algorithm. In: International Journal of Engineering and Advanced Technology. 2019 ; Vol. 8, No. 6 Special Issue. pp. 706-711.
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Brain tumor segmentation and classification using KNN algorithm. / Suhartono; Nguyen, Phong Thanh; Shankar, K.; Hashim, Wahidah; Maseleno, Andino.

In: International Journal of Engineering and Advanced Technology, Vol. 8, No. 6 Special Issue, 01.08.2019, p. 706-711.

Research output: Contribution to journalArticle

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