Brain Tumor Detection and Classification Via Kernel Support Vector Machine Using MRI Images

Abstract

Abstract: The MR images of the brain requires automatic and accurate classification for medical analysis and interpretation nowadays. Numerous methods have been declared already in the previous years. In this paper we have presented a method which classifies the brain image of MRI into normal and abnormal brain tumor images. This method uses wavelet transform that extract features from images. The next step involves principle component analysis (PCA) that reduces the feature dimensions. These reduced features are then employed to a kernel support vector machine (KSVM). 180 images of the brain were collected from the diseased which contained 130 abnormal brain and 50 normal brain images. Four kernels of different types were performed.