Classification of MR brain images using kernel support vector machine
Maske Hema Shivajirao, Sanjeev Dadarao Bhosale
PCA, DWT, KSVM, Kernel chose LIN, HPOL, IPOL, and RBF.
An automated and accurate analysis of MR brain images is really important for medical analysis and interpretation. Over the last decade multitudinous ways have before been proposed. In this paper, we presented a fully unique way to classify a given MR brain image as normal or abnormal. The proposed way first employed wave transubstantiate to yank features from images, followed by applying principle member analysis (PCA) to span back the width of features. The reduced features were submitted to a kernel support vector machine (KSVM). The strategy of K-fold stratified cross witness was used to enhance stereotype of KSVM. We chose seven common brain troubles (glioma, meningioma, Alzheimer's trouble, Alzheimer's trouble plus agnosia, Pick's trouble, sarcoma, and Huntington's trouble) as abnormal smarts. Original SVMs are linear classifiers. In this paper, we introduced the kernel SVMs (KSVMs), which extends original linear SVMs to nonlinear SVM classifiers by applying the kernel function to replace the dot product form in the original SVMs. The KSVMs allow us to transform, the transformation may be nonlinear and the transformed space high dimensional feature space, it may be nonlinear in the original input space.
Article Details
Unique Paper ID: 153054

Publication Volume & Issue: Volume 8, Issue 5

Page(s): 339 - 354
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