Brain Tumor MR Image Detection and Classification using Kernel SVM
B. Lalitha1, T. Ramashri2
1B. Lalitha, Research Scholar, Department of Electronics and Communication Engineering, S.V. University, Tirupati (Andhra Pradesh), India.
2Dr. T. Ramashri, Professor, Department of Electronics and Communication Engineering, S.V. University, Tirupati (Andhra Pradesh), India.
Manuscript received on 03 June 2019 | Revised Manuscript received on 28 June 2019 | Manuscript Published on 04 July 2019 | PP: 166-169 | Volume-8 Issue-1S4 June 2019 | Retrieval Number: A10330681S419/2019©BEIESP
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© 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: At Present Day the detection and classification of brain tumor MR Image is more complicated and time consuming task. Further experts are required for detection of tumor in brain which may lead to misclassification. Tumor in brain is an growth of unwanted cells which lead to cancer. In this paper k means clustering technique is used for Segmentation of tumor region from brain MRI . Features are extracted such as mean, median, skweness etc.. This Extracted feature will give more information content of tumor which helps in classification of tumor as Normal or Abnormal using Kernel SVM Classifier.
Keywords: MRI Image, GLCM, K Means Clustering, Kernel Support Vector Machine.
Scope of the Article: Image Security