Wavelet Based Feature Extraction and T-Set Evaluation for Automatic Brain Tumor Detection and Classification

Abstract

Brain image classification in order to detect various diseases like Tumor, stroke, Intracranial bleeding (ICB), etc., is being done manually from the Magnetic Resonance Imaging (MRI) results. However, the manual approaches are not accurate, tedious, and time consuming. The proposed work is to classify images using K-Nearest neighbor (KNN), Support vector machines (SVM), random forest, and Decision tree (DT) approaches, and the results obtained through these four approaches are compared. The input to the system is the MRI image, and it is preprocessed to remove various noise sources, then decomposed into structure and texture components. The Discrete wavelet transform (DWT) is applied to perform the noise removal, and decomposition of MRI images. Then classification techniques are applied on the decomposed images in order to detect the condition of the brain as normal, tumor, Alzheimer’s, and ICB. The classification techniques are implemented using R programming. The performance of the four approaches are measured in terms of precision, recall, and F-measure.