Analysis Of Performance Of Ensemble Based Machine Learning Algorithms For Classification Of Glioma Using MR Images

Abstract

Medical imaging poses a huge challenge for detecting abnormalities in MR Images. Image Classification mainly focuses on attaching one of the label from the existing set of class categories defined earlier. Inside the human brain, “gluey” tissues are the derivative of glioma. Many supervised and unsupervised classification algorithms are used for the detection of the tumor as benign or malignant. Usually, lighter datasets are used for image classification in the application field whereas comparatively larger and heavier datasets are used in the case of the medical field. The scope of this work is to identify an algorithm that shall be able to efficiently analyze the MRI image of a brain with a tumor in contrast with the traditional approach. The proposed work focuses on analyzing few deep learning algorithms that provide accurate results as expected. Also, an attempt has been made in the proposed work to scale up the size of the dataset and record the performance measure of each of these algorithms and in turn tweak the parameters to optimize them further by using few of the pre-processing techniques in combination.