Brain Tumor Segmentation Using U-Net Model

Abstract: Cancer of the brain presents a significant challenge due to its lethality, necessitating precise surgical segmentation. This study explores the application of U-Net, a Convolutional Neural Network (CNN) architecture, to segment brain tumors. Specifically, the segmentation involves distinguishing necrotic, edematous, growing, and healthy tissues within the brain. Analyzing overlaps between these diverse tissue types can be intricate, making the extraction of relevant information from images challenging. The study enhances the 2D U-Net network, leveraging the BraTS datasets for training. U-Net's flexibility in setting up various encoder and decoder routes proves invaluable for extracting pertinent information from images, adaptable for diverse applications