AI-based automated breast cancer segmentation in ultrasound imaging based on Attention Gated Multi ResU-Net.

ABSTRACT: Breast cancer is a leading cause of death among women worldwide, making early detection and diagnosis critical for effective treatment and improved patient outcomes. Ultrasound imaging is a common diagnostic tool for breast cancer, but interpreting ultrasound images can be challenging due to the complexity of breast tissue and the variability of image quality. This study proposed an Attention Gated Multi ResU-Net model for medical image segmentation tasks, that has shown promising results for breast cancer ultrasound image segmentation. The model's multi-scale feature extraction and attention-gating mechanism enable it to accurately identify and segment areas of abnormality in the breast tissue, such as masses, cysts, and calcifications. The model's quantitative test showed an adequate deg

SUBMITTER: Ding T 

PROVIDER: S-EPMC11623109 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature