  The paper discusses the use of MRI for segmentation techniques, specifically
focusing on brain tumor detection. It discusses the use of convolutional neural
networks (CNN) for automatic segmentation but also discusses challenges such as
non-isotropic resolution, Rician noise, and bias field effects. The paper
proposes models like VGG16, ResNet50, and ResU-net to predict MRI images based
on original and predicted masks. ResNet50 is found to be a promising model with
high accuracy and F1 score.
