Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet.

ABSTRACT: Segmentation of breast ultrasound images is a crucial and challenging task in computer-aided diagnosis systems. Accurately segmenting masses in benign and malignant cases and identifying regions with no mass is a primary objective in breast ultrasound image segmentation. Deep learning (DL) has emerged as a powerful tool in medical image segmentation, revolutionizing how medical professionals analyze and interpret complex imaging data. The UNet architecture is a highly regarded and widely used DL model in medical image segmentation. Its distinctive architectural design and exceptional performance have made it popular among researchers. With the increase in data and model complexity, optimization and fine-tuning models play a vital and more challenging role than before. This paper presents a

SUBMITTER: Khaledyan D 

PROVIDER: S-EPMC10718429 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature