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

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.