  Recently deep learning has been playing a major role in the field of computer
vision. One of its applications is the reduction of human judgment in the
diagnosis of diseases. Especially, brain tumor diagnosis requires high
accuracy, where minute errors in judgment may lead to disaster. For this
reason, brain tumor segmentation is an important challenge for medical
purposes. Currently several methods exist for tumor segmentation but they all
lack high accuracy. Here we present a solution for brain tumor segmenting by
using deep learning. In this work, we studied different angles of brain MR
images and applied different networks for segmentation. The effect of using
separate networks for segmentation of MR images is evaluated by comparing the
results with a single network. Experimental evaluations of the networks show
that Dice score of 0.73 is achieved for a single network and 0.79 in obtained
for multiple networks.
