Please use this identifier to cite or link to this item:
|Title:||Brain tumor segmentation and classification using KNN algorithm||Authors:||Suhartono
|Issue Date:||2019||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. © BEIESP.||DOI:||10.35940/ijeat.F1137.0886S19|
|Appears in Collections:||UNITEN Scholarly Publication|
Show full item record
Files in This Item:
|Brain tumor segmentation and classification using KNN algorithm.pdf||1.47 MB||Adobe PDF||View/Open|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.