  A brain tumor, whether benign or malignant, can potentially be life
threatening and requires painstaking efforts in order to identify the type,
origin and location, let alone cure one. Manual segmentation by medical
specialists can be time-consuming, which calls out for the involvement of
technology to hasten the process with high accuracy. For the purpose of medical
image segmentation, we inspected and identified the capable deep learning
model, which shows consistent results in the dataset used for brain tumor
segmentation. In this study, a public MRI imaging dataset contains 3064
TI-weighted images from 233 patients with three variants of brain tumor, viz.
meningioma, glioma, and pituitary tumor. The dataset files were converted and
preprocessed before indulging into the methodology which employs implementation
and training of some well-known image segmentation deep learning models like
U-Net & Attention U-Net with various backbones, Deep Residual U-Net, ResUnet++
and Recurrent Residual U-Net. with varying parameters, acquired from our review
of the literature related to human brain tumor classification and segmentation.
The experimental findings showed that among all the applied approaches, the
recurrent residual U-Net which uses Adam optimizer reaches a Mean Intersection
Over Union of 0.8665 and outperforms other compared state-of-the-art deep
learning models. The visual findings also show the remarkable results of the
brain tumor segmentation from MRI scans and demonstrates how useful the
algorithm will be for physicians to extract the brain cancers automatically
from MRI scans and serve humanity.
