Performance Analysis of KNN Classifier with and Without GLCM Features In Brain Tumor Detection
Bharanidharan N1, Harikumar Rajaguru2, Geetha V3
1Bharanidharan N, Department of Electrical and Electronics Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India.
2Harikumar Rajaguru, Department of Electrical and Electronics Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India.
3Geetha V, Department of Electrical and Electronics Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India.
Manuscript received on 01 December 2018 | Revised Manuscript received on 06 December 2018 | Manuscript Published on 26 December 2018 | PP: 103-106 | Volume-8 Issue- 2S2 December 2018 | Retrieval Number: BS2020128218/19©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: This paper presents the K-Nearest Neighbour (KNN) classifier combined with Grey Level Co-occurrence Matrix(GLCM) feature extraction technique for brain tumor detection using MATLAB software. Thirty MRI images obtained from the clinical sources are analyzed in this study. Though KNN classifier is less time consuming, it has a disadvantage of less accuracy. To improve the accuracy of KNN classification, GLCM feature extraction is used. Performance of KNN classifier is analyzed with and without GLCM feature extraction.
Keywords: Tumor Detection- Classifer- Feature Extraction-KNN-GLCM.
Scope of the Article: Computer Architecture and VLSI