U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

Alzheimer's disease (AD) is a prevalent, irreversible, chronic, and degenerative disorder
whose diagnosis at the prodromal stage is critical. Mostly, single modality data, such as …

Purpose: This paper proposes a new network framework called EAR-U-Net, which
leverages EfficientNetB4, attention gate, and residual learning techniques to achieve …

Technological breakthroughs in computing have empowered vision-based surveillance
systems to detect fire using transformers framework. Over the last few decades …

Detecting defect patterns in semiconductors is very important for discovering the
fundamental causes of production defects. In particular, because mixed defects have …

Background Multimodality positron emission tomography/computed tomography (PET/CT)
imaging combines the anatomical information of CT with the functional information of PET. In …

Intracerebral hemorrhage (ICH) poses a severe threat to human health and well-being.
Automatic segmentation of hematomas in CT images can provide essential diagnostic …

Simple Summary In our study, we aimed to create an accurate segmentation algorithm of
blood vessels within histologically stained tumor tissue using deep learning. Blood vessels …

Automatic and accurate lesion segmentation is critical to the clinical estimation of the lesion
status of stroke diseases and appropriate diagnostic systems. Although existing methods …

Deep neural networks are now widely used in the medical image segmentation field for their
performance superiority and no need of manual feature extraction. U-Net has been the …