MRI Based Automated Detection of Brain Tumor Using DWT, GLCM, PCA, Ensemble of SVM and PNN in Sequence

Abstract

Challenging, iterative, error-prone and time-consuming process it is to classify, segment, and detect the area of infection in MRI images of brain tumors. Moreover, to visualize and numerically quantify the properties of the structure of the abnormal human brain even with sophisticated Magnetic Resonance Imaging techniques requires advanced and expensive tools. MRI can better differentiate and clarify the neuronal architecture of the human brain compared to other imaging methodologies. In this study, a complete pipeline is proposed to classify abnormal structures in the human brain from MRI images that might be early signs of tumor formation. The proposed pipeline consists of noise reduction techniques, gray-level matrix (GLCM) extraction features, segmentation of DWT-based brain tumor areas reducing complexity, and Support Vector Machine (SVM) using the Radial Basis Function kernel (RBF) in ensemble with PNN for classification. SVM and PNN in combination provide a data-driven prediction model of the possible existence and location of a brain tumor in MRI images Experimental results achieved nearly 99% accuracy in identifying healthy and tumorous tissue based on structure from brain MRI images. The proposed method together with comparable accuracy is reasonably lightweight and fast compared to the other existing deep learning-based methods.