  Intracranial tumors are groups of cells that usually grow uncontrollably. One
out of four cancer deaths is due to brain tumors. Early detection and
evaluation of brain tumors is an essential preventive medical step that is
performed by magnetic resonance imaging (MRI). Many segmentation techniques
exist for this purpose. Low segmentation accuracy is the main drawback of
existing methods. In this paper, we use a deep learning method to boost the
accuracy of tumor segmentation in MR images. Cascade approach is used with
multiple scales of images to induce both local and global views and help the
network to reach higher accuracies. Our experimental results show that using
multiple scales and the utilization of two cascade networks is advantageous.

Image and Video Processing;Computer Vision and Pattern Recognition