Identification And Classification Of Brain Tumor Images Using Efficient Classifier
B. Sathees kumar
Dr. B. Sathees kumar, Associate Professor of Computer Science Bishop Heber College, Trichy, (Tamil Nadu), India.
Manuscript received on February 01, 2019. | Revised Manuscript received on February 14, 2019. | Manuscript published on August 30, 2019. | PP: 3677-3683 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9373088619/19©BEIESP | DOI: 10.35940/ijeat.F9373.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The main objective of this study is to propose a model for finding brain tumor. Failing to detect the tumor in its prior stage will increases the chance of losing a life. So the identification and treatment for the tumor in its prior stages become vital to save the life of a human being. This work uses the Magnetic Resonance Image (MRI) images to identify and classify the benign and malign type brain tumors. Low pass filter is applied to preprocess the MRI image that removes the unwanted background structures at the same time keeps the important portions sharpened. Watershed segmentation method is used for segmenting the tumor affected area independently. The statistical feature extraction method Gray Level Co-occurrence Matrix (GLCM) is applied to take out the imperative features from the segmented tumor. The feature selection is performed using Recursive Feature Elimination- Particle Swarm Optimization (RFE-PSO) method. Ensemble Support Vector Machine (SVM) is applied to classify the tumors into harmless and harmful from the medical image.
Keywords: Magnetic Resonance Imaging, Low pass Filter, Watershed segmentation, GLCM, RFE-PSO, Ensemble SVM.