DOIONLINE 
DOIONLINE Publish In |
International Journal of Advances in Science, Engineering and Technology(IJASEAT)-IJASEAT |
 Journal Home Volume Issue |
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Issue |
Volume-3, Issue-4 ( Oct, 2015 ) | |||||||||
Paper Title |
Automatic Brain Tumor Detection And Classification Using Svm Classifier | |||||||||
Author Name |
Sonu Suhag, Lalit Mohan Saini | |||||||||
Affilition |
School of Biomedical Engineering, National Institute of Technology, Kurukshetra, Haryana - 136119, INDIA | |||||||||
Pages |
119-123 | |||||||||
Abstract |
Tumor is unwanted growth of unhealthy cell which increase intracranial pressure within skull. Medical image processing is the most challenging and innovative field specially MRI imaging modalities. The strategy presented in this paper involves preprocessing, segmentation, feature extraction, detection of tumor and its classification from MRI scanned brain images. Magnetic Resonance Imaging (MRI) is a non-invasive imaging modalities which is best suited for the detection of brain tumor. The segmentation method proposed in this paper is fuzzy c-means (FCM) which can improve medical image segmentation. The algorithm is easy to handle and identification of tumor and its classification in scanned region has been done accurately. A user friendly environment has been created by using GUI in MATLAB resulting in an automated brain tumor detection system for MRI scanned images. By using the GUI tool, the physician and other practitioners are facilitated in detecting the tumor and its geometrical feature extraction. Multi-SVM has used to classify the various type of tumors like Gliomas, Metastasis, Astrocytoma etc. In this work, Multi Support Vector Machines (m-SVMs) has been proposed and applied to brain scanned image slices classification using features derived from slices. This work helps in recognition of tumor which in turn saves the precious time of medical diagnostic to diagnose the tumor automatically in short span of time. Keywords— Image Classification, Medical Diagnosis, Tumors. | |||||||||
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