Deep Learning for Brain Tumor Segmentation using Magnetic Resonance Images

Abstract

Cancer is one of the most significant causes of death worldwide, accounting for millions of deaths each year. The fatality rate of cancer is getting higher. Over the last three decades, deep neural networks have been critical in cancer research. This article described the development of a system for fully automated segmentation of brain tumor. In this study, we have proposed a unique ensemble of Convolutional Neural Networks (ConvNet) for segmenting gliomas from MR images. Two fully linked ConvNets constituted the ensemble model (2D-ConvNet and 3-D ConvNet). The novel model is validated against a single dataset from the Brain Tumor Segmentation (BraTS) challenge, specifically BraTS_2018. The prediction results obtained using the proposed methodology on the BraTS_2018 datasets demonstrate the suggested architecture's efficiency.