Segmentation and Detection of Brain Tumor by using Machine Learning
Priyanka Arya1, Satyasundara Mahapatra2, Anil Kumar Malviya3
1Priyanka Arya, Department of Computer Science & Engineering, Kamla Nehru Institute of Technology, Sultanpur, India.
2Satyasundara Mahapatra, Department of Computer Science and Engineering, Pranveer Singh Institute of Technology, Kanpur, India.
3Dr. Anil Kumar Malviya, Department of Computer Science & Engineering, Kamla Nehru Institute of Technology, Sultanpur, India. 

Manuscript received on November 20, 2019. | Revised Manuscript received on November 26, 2019. | Manuscript published on 30 November, 2019. | PP: 3226-3235 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8038118419/2019©BEIESP | DOI: 10.35940/ijrte.D8038.118419

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Abstract: The segmentation and detection of brain pathologies in medical images is an indispensible step. This helps the radiologist to diagnose a variety of brain deformity and helps in the set up for a suitable treatment. Magnetic Resonance Imaging (MRI) plays a significant character in the research area of neuroscience. The proposed work is a study and probing of different classification techniques used for automated detection and segmentation of brain tumor from MRI in the field of machine learning. This paper try to present the feature extraction from raw MRI and fed the same to four classifier named as, Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbors (KNN), and Artificial Neural Network (ANN). This mechanism was done in various stages for Computer Aided Detection System. In the preliminary stage the pre-processing and post-processing of MR image enhancement is done. This was done as the processed image is more likely suitable for the analysis. Then the k-means clustering is used to sectioning the MRI by applied mean gray level method. In the subsequent stage, statistical feature analysis were done, the features were computed using Haralick’s equation for feature based on the Gray Level Co-occurrence Matrix. Feature chosen dependent on tumor region, location, periphery, and color from the sectioned image is then classified by applying the classification techniques. In the third stage SVM, DT, ANN, and KNN classifiers were used for diagnoses. The performances of the classifiers are tested and evaluated successfully.
Keywords: Artificial Neural Network, Decision Tree, k-Nearest Neighbors, Machine Learning, Magnetic Resonance Image, Support Vector Machine.
Scope of the Article: Machine Learning.