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Title of Journal: Multimed Tools Appl

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Abbravation: Multimedia Tools and Applications

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Springer US

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DOI

10.1007/bf01132802

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1573-7721

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Pathological brain detection using curvelet featur

Authors: Deepak Ranjan Nayak Ratnakar Dash Banshidhar Majhi
Publish Date: 2016/11/28
Volume: 77, Issue: 3, Pages: 3833-3856
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Abstract

This paper aims at developing an automatic pathological brain detection system PBDS to assist radiologists in identifying brain diseases correctly in less time Magnetic resonance imaging MRI has the potential to provide better information about the brain soft tissues and hence MR images have been incorporated in the proposed system Fifty largest coefficients are selected from each subband of a level5 fast discrete curvelet transform FDCT to serve as a feature set for each image To reduce the size of the feature set principal component analysis PCA has been harnessed Subsequently least squares SVM LSSVM with three different kernels are utilized to segregate the images as healthy or pathological The proposed system has been validated on three benchmark datasets and a 10 ×kfold stratified cross validation SCV test has been performed It indicates that the proposed system “FDCT + PCA + LSSVM + RBF” achieves better performance than not only two other systems having linear and polynomial kernel but also 22 existing methods In addition the suggested system requires only six features which are computationally economical for a practical use

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