Automatic Detection System for Brain Tumor Through Wavelet Transform and Support Vector Machine

A tumor is an abnormal growth of harmful cells that increases intracranial pressure in the skull. Automatic tumor detection using Magnetic Resonance Imaging (MRI) is essential for accurate diagnosis and effective treatment. This study employs discrete wavelet transform (DWT) and principal component analysis (PCA) for feature extraction and feature reduction, respectively. The classification utilizes a kernel Support Vector Machine (SVM) which is trained and tested on new MRI brain images. The proposed method's effectiveness is demonstrated through comparisons with different kernels.