Brain Tumor Segmentation and Classification using KNN Algorithm
Suhartono1, Phong Thanh Nguyen2, K. Shankar3, Wahidah Hashim4, Andino Maseleno5
1Suhartono, Department of Informatics and Computer Education, Study Program, Universitas Negeri Makassar, Indonesia.
2Phong Thanh Nguyen, Department of Project Management, Ho Chi Minh City Open University, Vietnam.
3K. Shankar, Department of Computer Applications, Alagappa University, India.
4Wahidah Hashim, Department of Computing Energy, Institute of Informatics Universiti, Tenaga Nasional, Malaysia.
5Andino Maseleno, Department of Computing Energy, Institute of Informatics Universiti, Tenaga Nasional, Malaysia.
Manuscript received on 16 August 2019 | Revised Manuscript received on 28 August 2019 | Manuscript Published on 06 September 2019 | PP: 706-711 | Volume-8 Issue- 6S, August 2019 | Retrieval Number: F11370886S19/19©BEIESP | DOI: 10.35940/ijeat.F1137.0886S19
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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.
Keywords: Cancer Detection, kNN Algorithm, Segmentation.
Scope of the Article: Classification