Segmentation of Brain Tumor using Deep Learning Techniques

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.