Detection and Classification of Brain Tumor
This work presented a novel method to classify a given MR brain image as normal or abnormal, which first employed wavelet transforms to extract features from images, followed by applying Principle Component Analysis (PCA) to reduce the dimensions of features.
Abstract
--------------------------------------------------------------------- Abstract - The brain is one of the most complex organs in the human body that works with billions of cells. A cerebral tumor occurs when there is an uncontrolled division of cells that form an abnormal group of cells around or within the brain. This cell group can affect the normal functioning of brain activity and can destroy healthy cells. Automated and accurate classification of MRI brain images is extremely important for medical analysis and interpretation. Over the last decade numerous methods have already been proposed. We presented a novel method to classify a given MR brain image as normal or abnormal. The proposed method first employed wavelet transforms to extract features from images, followed by applying Principle Component Analysis [3] (PCA) to reduce the dimensions of features. The reduced features were submitted to a Kernel Support Vector Machine (KSVM) [5]. The strategy of K – fold [4] stratified cross validation was used to enhance generalization of KSVM.