Brain Tumor Segmentation by Cascaded Deep Neural Networks Using Multiple
Image Scales
- URL: http://arxiv.org/abs/2002.01975v1
- Date: Wed, 5 Feb 2020 20:00:40 GMT
- Title: Brain Tumor Segmentation by Cascaded Deep Neural Networks Using Multiple
Image Scales
- Authors: Zahra Sobhaninia, Safiyeh Rezaei, Nader Karimi, Ali Emami, Shadrokh
Samavi
- Abstract summary: Intracranial tumors are groups of cells that usually grow uncontrollably.
Early detection and evaluation of brain tumors is an essential preventive medical step that is performed by magnetic resonance imaging (MRI)
- Score: 12.463038471051478
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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