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BRAIN TUMOR CLASSIFICATION USING DATA MINING ALGORITHMS

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Bibliographic Details
Title: BRAIN TUMOR CLASSIFICATION USING DATA MINING ALGORITHMS
Authors: Kalyani A. Bhawar*, Prof. Ajay S. Chhajed
Publisher Information: Zenodo
Publication Year: 2016
Collection: Zenodo
Subject Terms: MRI, Decision Tree, CART and Random tree Algorithm
Description: The classification of brain tumor in the magnetic resonance imaging (MRI) is very important for detecting the existence and outlines of tumors. In this paper, an algorithm about brain tumor classification is based on the metabolite values of brain MRI image is presented. Our goal is to calculate vector patterns from the metabolite values and classify the tumors automatically .Decision Trees are considered to be one of the most popular approaches for representing classifiers. Statistics, machine learning, pattern recognition, and Data Mining have dealt with the issue of growing a decision tree from available data. The purpose of this work is to present an updated survey of current methods for constructing decision tree for classifying brain tumors. The main focus is on solving the cancer classification problem using single decision tree classifiers (CART and Random algorithm).
Document Type: article in journal/newspaper
Language: unknown
Relation: https://zenodo.org/records/165011; oai:zenodo.org:165011; https://doi.org/10.5281/zenodo.165011
DOI: 10.5281/zenodo.165011
Availability: https://doi.org/10.5281/zenodo.165011
https://zenodo.org/records/165011
Rights: Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode
Accession Number: edsbas.CC403DF3
Database: BASE
Description
Abstract:The classification of brain tumor in the magnetic resonance imaging (MRI) is very important for detecting the existence and outlines of tumors. In this paper, an algorithm about brain tumor classification is based on the metabolite values of brain MRI image is presented. Our goal is to calculate vector patterns from the metabolite values and classify the tumors automatically .Decision Trees are considered to be one of the most popular approaches for representing classifiers. Statistics, machine learning, pattern recognition, and Data Mining have dealt with the issue of growing a decision tree from available data. The purpose of this work is to present an updated survey of current methods for constructing decision tree for classifying brain tumors. The main focus is on solving the cancer classification problem using single decision tree classifiers (CART and Random algorithm).
DOI:10.5281/zenodo.165011