Brain Tumor Segmentation and Classification using Hybrid clustering technique and SVM Classifier
Maibam Mangalleibi Chanu1, Khelchandra Thongam2
1Maibam Mangalleibi Chanu, Department of Computer Science and Engineering, National Institute of Technology, Imphal (Manipur), India.
2Dr. Khelchandra Thongam, Department of Computer Science and Engineering, National Institute of Technology, Imphal (Manipur), India.
Manuscript received on 22 November 2019 | Revised Manuscript received on 03 December 2019 | Manuscript Published on 14 December 2019 | PP: 14-17 | Volume-9 Issue-1S November 2019 | Retrieval Number: A10041191S19/2019©BEIESP | DOI: 10.35940/ijitee.A1004.1191S19
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Brain tumor Detection is a primary concern in today’s life. So a computer aided technology must be implemented for an accurate detection and identification of brain tumor. The tumor can be detected using various classification techniques from brain MR Images. In this paper segmentation process is being done using K means Clustering technique and Binary Thresholding, the features from the images are then extracted using GLCM where six texture features are extracted and SVM Classifier is being used for classification of the images. This proposed method shows an accuracy of 97.12%.
Keywords: GLCM (Gray Level Co-Occurrence Matrix), SVM (Support Vector Machine), MRI (Magnetic Resonance Imaging).
Scope of the Article: Clustering