A Novel Method using Convolutional Neural Network for Segmenting Brain Tumor in MRI Images

Abstract

ISSN: 2321-2381 © 2017 | Published by The Standard International Journals (The SIJ) 48 Abstract—Among brain tumors, glioma area unit the foremost common and aggressive, resulting in a awfully short life in their highest grade. Thus, treatment designing may be a key stage to enhance the quality of lifetime of oncologic patients. Magnetic Resonance Imaging (MRI) may be a wide used imaging technique to assess these tumors, however the big quantity of knowledge created by magnetic resonance imaging prevents manual segmentation in a very affordable time, limiting the use of precise quantitative measurements within the clinical apply. In this paper, we tend to propose associate degree automatic segmentation methodology based on Convolutional Neural Network, exploring small 3x3 kernels. The employment of small kernels permits coming up with a deeper architecture, besides having a positive impact against over fitting, given the less variety of weights within the network. We also investigated the employment of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, well-tried in conjunction with information augmentation to be terribly effective for neoplasm segmentation in magnetic resonance imaging pictures.