Convolutional neural networks have gained tremendous success in computer vision and
medical imaging applications. To make these models truly portable and compatible for …

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 …

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

Deep learning techniques, particularly convolutional neural networks, have shown great
potential in computer vision and medical imaging applications. However, deep learning …

Convolutional neural network (CNN) such as U-Net has demonstrated excellent
performance for medical image segmentation. However, there are some limitations of its …

Recently, ConvNeXts constructing from standard ConvNet modules has produced
competitive performance in various image applications. In this paper, an efficient model …

Medical image segmentation plays a vital role in various clinical applications, enabling
accurate delineation and analysis of anatomical structures or pathological regions …

Convolutional neural networks (CNNs) and Transformer-based models are being widely
applied in medical image segmentation thanks to their ability to extract high-level features …

Accurate segmentation is a basic and crucial step for medical image processing and
analysis. In the last few years, U-Net, and its variants, have become widely adopted models …

Biomedical image segmentation plays a central role in quantitative analysis, clinical
diagnosis, and medical intervention. In the light of the fully convolutional networks (FCN) …