Expected Title: Brain Tumor prediction using
Machine learning algorithm
Abstract:
Brain Tumor is one of the rapid life threatening diseases challenging millions of people. MRI
(Magnetic Resonance Imaging) is a medical technique, mainly used by the radiologist for
visualization of internal structure of the human body without any surgery. MRI provides plentiful
information about the human soft tissue, which helps in the diagnosis of brain tumour. Accurate
classification and segmentation of MRI image is important for the diagnosis of brain tumor by
computer aided clinical tool. The classification and segmentation techniques should provide High
Level Accuracy while performing Tumor prediction. These Techniques should be effective for
Real-Time applications. The Growing urge for saving Time is equally important as the Desired
Accuracy Level of the Result. Unfortunately, open data sets for designing and testing these
algorithms are not currently available, and private data sets differ so widely that it is hard to
compare the different tumor strategies. The critical factors leading to these differences include,
but not limited to, i) the imaging modalities employed, ii) the type of the tumor (GBM or LGG,
primary or secondary tumors, solid or infiltrative growing), and iii) the state of disease (images
may not only be acquired prior to treatment, but also post‐operatively and therefore show
radiotherapy effects and surgically‐imposed cavities). BraTS Data Set provide more routine
clinically-acquired 3T multimodal MRI scans and all the ground truth labels have been manually-
revised by expert board-certified neuroradiologists. But most of the tumor data sets are high
Dimensional data. Machine Learning is one of the solution to handle High Dimensional data set
and provide accurate prediction of the tumor classification. Feature selection is one of difficult
task to handle high dimensional data set for classification. To improve the accuracy of prediction
proposed feature selection based brain tumor prediction using machine learning algorithms.
Keyword : Classification, Segmentation, Feature section, BraTS.
