It compares the members of two sets to determine the shared and unique members. The more similar the two sets are, the higher the percentage.
Published in Chapter:
Brain Tumor Segmentation Using Deep Learning Technique: 2D U-Net Model Variant for Tumor Segmentation
Muhammad Hashir Khan (COMSAT University Islamabad, Lahore, Pakistan), Aksam iftikhar (COMSATS University Islamabad, Lahore, Pakistan), and Tayyab Wahab (COMSAT University Islamabad, Lahore, Pakistan)
Copyright: © 2023
|Pages: 14
DOI: 10.4018/978-1-6684-6434-2.ch003
Abstract
Cancer is one of the most lethal diseases in the world. A brain tumor is a form of cancer that develops in the brain's glial cells. Magnetic resonance imaging (MRI) is a prominent imaging tool for detecting brain tumors. It includes four different modalities that neurologists use to determine the location and kind of tumor. The suggested approach uses a 2D U-Net model to separate the brain tumors sub regions. To prevent excessive preprocessing and GPU utilization, the authors utilize the patching approach to partition the picture slices into distinct patches in this study. Second, they leverage the squeeze and excitation blocks to more effectively map low-level features to high-level features than a basic U-Net. The suggested technique yields DICE scores of 0.85, 0.87, and 0.90 for the three tumor categories of enhancing tumor, whole tumor, and tumor core, respectively. The results outperform the most recent approaches, including the major papers from the Brats 2019 competition.