A Hybrid Framework for Brain Tumor Classification using Grey Wolf Optimization and Multi-Class Support Vector Machine
Arun Kumar1, M.A.Ansari2, Alaknanda Ashok3

1Arun Kumar, Department of Computer Science and Engg,Uttrakhand Technical University, Dehradun India.
2M.A.Ansari,Department of Electrical Engg., Gautam Buddha University, G. Noida, India.
3Alaknanda Ashok, Director, W.I.T. Dehradun, India. 

Manuscript received on 03 August 2019. | Revised Manuscript received on 09 August 2019. | Manuscript published on 30 September 2019. | PP: 7746-7752 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6315098319/2019©BEIESP | DOI: 10.35940/ijrte.C6315.098319

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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: Medical image processing has a vital role in the detection of diseases in human beings. The accuracy for disease detection using any medical image is highly dependent on the image processing methods. Features extraction and reduction are the two key steps during the medical image processing for disease classification. To develop an effective and efficient mechanism with high accuracy for classification of malignant brain tumor from Magnetic Resonance Imaging (MRI) is the objective of the present research. To achieve this, a nature inspired algorithm; namely, Grey Wolf Optimization (GWO) along with a classification method, multiclass Support Vector Machine (MSVM) is used. Further, Results for the classification accuracy obtained from GWO are compared with other two well-known optimization algorithms such as Particle Swarm Optimization (PSO) and Firefly Algorithm (FA).
Keywords: Brain Tumor, Feature Extraction and Reduction, Grey Wolf Optimization, SVM, MRI Images.

Scope of the Article: Classification