Brain tumor feature extraction and edge enhancement algorithm based on U-Net network.

ABSTRACT:

Background

Statistics show that each year more than 100,000 patients pass away from brain tumors. Due to the diverse morphology, hazy boundaries, or unbalanced categories of medical data lesions, segmentation prediction of brain tumors has significant challenges.

Purpose

In this thesis, we highlight EAV-UNet, a system designed to accurately detect lesion regions. Optimizing feature extraction, utilizing automatic segmentation techniques to detect anomalous regions, and strengthening the structure. We prioritize the segmentation problem of lesion regions, especially in cases where the margins of the tumor are more hazy.

Methods

The VGG-19 network structure is incorporated into the coding stage of the U-Net, resulting in a deeper network structure, and an attention mech

SUBMITTER: Cheng D 

PROVIDER: S-EPMC10687284 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature