Brain Tumor MRI Segmentation and Classification Using Ensemble Classifier
Parasuraman Kumar1, B. Vijay Kumar2

1Parasuraman Kumar, Assistant Professor, Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunalveli (Tamil Nadu), India.
2B. Vijay Kumar, Research Scholar, Manonmaniam Sundaranar University, Tirunalveli (Tamil Nadu), India.
Manuscript received on 03 June 2019 | Revised Manuscript received on 28 June 2019 | Manuscript Published on 04 July 2019 | PP: 244-252 | Volume-8 Issue-1S4 June 2019 | Retrieval Number: A10440681S419/2019©BEIESP
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Abstract: Brain tumor is a group of tissue that is prearranged by a slow addition of irregular cells. It occurs when cell get abnormal formation within the brain. Recently it is becoming a major cause of death of many people. The seriousness of brain tumor is very big among all the variety of cancers, so to save a life immediate detection and proper treatment to be done. Detection of these cells is a difficult problem, because of the formation of the tumor cells. It is very essential to compare brain tumor from the MRI treatment. It is very difficult to have vision about the abnormal structures of human brain using simple imaging techniques. Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensemble methods combine the procedure of neural network, extreme learning machine (ELM) and support vector machine classifiers. The proposed system consists of manifold phases. Preprocessing, segmentation, feature extraction, and classification. At initially preprocessing is performed by using filtering algorithm. Secondly segmentation is performed by using clustering algorithm. Thirdly feature extraction is performed by Gray Level Co-Occurrence Matrix (GLCM). Automatic brain tumor stage is performed by using ensemble classification. This phase classifies brain images into tumor and non-tumors using Feed Forwarded Artificial neural network based classifier. Experiments have exposed that the method was more robust to initialization, faster and accurate.
Keywords: Ensemble classifiers, GLCM, ELM, SVM, Feed Forward Artificial Neural Network and Fuzzy C-means Clustering.
Scope of the Article: Classification