Open AccessBRAIN TUMOR SEGMENTATION USING DEEP CONVOLUTION NEURAL NETWORK AND SUPPORT VECTOR MACHINE
Author(s) -
Nisha Joseph,
Deepak Murugan,
Basil John Thomas,
A. Ramya
Publication year - 2021
Publication title -
journal of critical reviews
Language(s) - English
Resource type - Journals
ISSN - 2394-5125
DOI - 10.31838/jcr.07.19.71
Subject(s) - artificial intelligence , support vector machine , computer science , segmentation , deep learning , convolutional neural network , pattern recognition (psychology) , dice , artificial neural network , convolution (computer science) , task (project management) , sensitivity (control systems) , machine learning , mathematics , engineering , geometry , systems engineering , electronic engineering
Tumor segmentation is the primary and tedious task for the clinical experts. Computer Aided Design is the only solution which identifies the tumor very accurately with less time. Deep learning models such as the convolutionary neural network have been widely used in 3D biomedical segmentation and have achieved state-of-the-art performance.In this research, saliency based deep features are extracted from MRI. Then Support Vector Machine is used for classifying deep features. The proposed method is tested on BRATS 2015 dataset and it is compared with state-of-methods and recent methods. The proposed method achieves 0.94, 0.93 and 0.9 as dice score, precision and sensitivity respectively which is greater than other methods.