  This paper discusses the deep learning architectures currently used for
pixel-wise segmentation of primary and secondary glioblastomas and low-grade
gliomas. We implement various models such as the popular UNet architecture and
compare the performance of these implementations on the BRATS dataset. This
paper will explore the different approaches and combinations, offering an in
depth discussion of how they perform and how we may improve upon them using
more recent advancements in deep learning architectures.
