Application of multi- path CNN for brain tumor segmentation from MRI

Abstract

The automation of brain tumor segmentation from MRI is an active topic in the field of medical research. Different approaches and methods are being proposed throughout the years to address this challenging task. The application of convolutional neural network has caught the attention of many researchers for the solution of this particular problem due to its extraordinary performances in the field of computer vision . Many of the state-of-the-art techniques use different approaches based on CNN. One of such approaches is the multi-path CNN architecture. In this paper, we propose a novel multi-path CNN architecture that allows flow of information in two different pathways resulting in the exploitation of both local and global features simultaneously, hence this architecture can also be called two-pathway architecture. We use this architecture to train on the dataset collected from BRATS 2013 challenge. Our model when tested on BRATS 2013 training images, showed on an average 95 .654% accuracy.