Objective To develop and validate a novel convolutional neural network (CNN) termed
“Super U-Net” for medical image segmentation. Methods Super U-Net integrates a dynamic …

Since medical imaging is a fundamental step in clinical diagnosis and treatment, medical
image processing is an attractive field for researchers. Among the different applications of …

Deep learning (DL)-based semantic segmentation methods have been providing state-of-
the-art performance in the past few years. More specifically, these techniques have been …

In intelligent perception and diagnosis of medical equipment, the visual and morphological
changes in retinal vessels are closely related to the severity of cardiovascular diseases (eg …

This paper proposes a novel U-Net variant using stacked dilated convolutions for medical
image segmentation (SDU-Net). SDU-Net adopts the architecture of vanilla U-Net with …

In this study, we propose LDMRes-Net, a lightweight dual-multiscale residual block-based
convolutional neural network tailored for medical image segmentation on IoT and edge …

Deep learning (DL) based semantic segmentation methods have been providing state-of-the-
art performance in the last few years. More specifically, these techniques have been …

Medical image segmentation plays a vital role in computer-aided diagnosis procedures.
Recently, U-Net is widely used in medical image segmentation. Many variants of U-Net have …

Deep learning has been widely utilized for medical image segmentation. The most
commonly used U-Net and its variants often share two common characteristics but lack solid …

Medical image segmentation is a fundamental task in medical image analysis. Dynamic
receptive field is very helpful for accurate medical image segmentation, which needs to be …