Brain MRI detection by Sematic Segmentation models- Transfer Learning approach

Abstract

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.