Abstract:
Cancer is one of the fatal types of disease in the World. A brain tumor is a type of
cancer that appears in the glial cells of the brain. There exist two types of brain tumors.
One is High-grade glioma and the other is low-grade glioma. The survival rate for HGG
is very low while according to history most patients diagnose with LGG survive this
disease. To detect the tumor in the brain one of the popular techniques is the Magnetic
Imaging Resonance (MRI). It has four different modalities and neurologists after
observing the different MRI modalities diagnose the tumor location and category. But
manually detecting the tumor from MRI scans is a difficult task. There is always a need
to segment the tumor region automatically from an MRI scan. To solve this problem,
many researchers proposed different solutions. Many researchers use deep learning
models to address this issue. One of the earliest networks used for this was
Convolutional Neural Network (CNN) and it gives good results for tumor segmentation
tasks.
In this research, the approach we used is based on the ensemble method. In this
approach, we use three different U-Net models to train them from scratch and predict
the results on each model separately. After that, we ensemble all three models predicted
results applying the majority voting technique and produce a result on testing data. Our
proposed ensemble method produces a dice score of 0.86, 0.88, 0.89, and Hausdorff
distance 2.0, 2.0., 2.0 for the three categories of a tumor, Enhancing tumor, Whole
tumor, and Tumor core respectively on the testing dataset. Our results are better than
many comparable state of the art method including the Brats 2019 challenge prominent
papers.
