An Efficient Brain Stroke Image Classification Model Based on Artificial Bee Colony Optimization with Kernel Support Vector Machine

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.