The advanced development of deep learning methods has recently made significant
improvements in medical image segmentation. Encoder–decoder networks, such as U-Net …

Current state-of-the-art medical image segmentation techniques predominantly employ the
encoder–decoder architecture. Despite its widespread use, this U-shaped framework …

Automatic segmentation of medical images is a difficult task in the field of computer vision
owing to the various backgrounds, shapes, size, and colors of polyps or tumors. Despite the …

Medical image segmentation is a fundamental step in medical analysis and diagnosis. In
recent years, deep learning networks have been used for precise segmentation. Numerous …

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 …

U-Net is a widely adopted neural network in the domain of medical image segmentation.
Despite its quick embracement by the medical imaging community, its performance suffers …

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 …

Background and objective Convolutional neural networks (CNNs) play an important role in
the field of medical image segmentation. Among many kinds of CNNs, the U-net architecture …

Many current and state-of-the-art deep learning models for accurate image segmentation
are based on the U-Net architecture, a convolutional neural network designed for …

Although various methods based on convolutional neural networks have improved the
performance of biomedical image segmentation to meet the precision requirements of …