An Efficient Brain Stroke Image Classification Model Based on Artificial Bee Colony Optimization with Kernel Support Vector Machine
S. Manikandan1, P. Dhanalakshmi2, R. Thiruvengatanadhan3

1S. Manikandan, Assistant Professor, Department of CSE, D.D.E, Annamalai University, Annamalai nagar, Tamilnadu, India.
2P. Dhanalakshmi, Professor, Department of CSE, Annamalai University, Annamalai nagar, Tamilnadu, India.
3R. Thiruvengatanadhan, Assistant Professor, Department of CSE, Annamalai University, Annamalai nagar, Tamilnadu, India.

Manuscript received on 24 August 2019. | Revised Manuscript received on 18 September 2019. | Manuscript published on 30 September 2019. | PP: 1274-1284 | Volume-8 Issue-11, September 2019. | Retrieval Number: J94960881019/2019©BEIESP | DOI: 10.35940/ijitee.J9496.0981119
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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: In present decade, identification of abnormalities in brain gains significant attention for medical diagnosis. Though numerous existing models are available, only a few methods have been proposed which classifies a set of different kinds of brain defects. This paper introduces an efficient hybridization model for classifying the provided MR brain image as normal or abnormal. The presented model initially makes use of digital wavelet transform (DWT) for extracting features and utilizes principal component analysis (PCA) for feature space reduction. Next, a kernel support vector machine (KSVM) with radial basis function (RBF) kernel is built by artificial bee colony (ABC) for optimizing the parameters namely C and σ. For experimentation, 5-fold cross validation procedure is involved and a detailed investigation of the results takes place by comparing it with the existing models. To select the parameters, ABC algorithm has undergone a comparison with the random selection approach. The presented model is tested using a benchmark MR brain dataset. The experimental values indicated that the ABC is highly efficient for constructing optimal KSVM.
Keywords: Brain stroke; Artificial Bee Colony; Support Vector Machine; MRI; Classifier.
Scope of the Article: