Image segmentation is a branch of digital image processing which has numerous
applications in the field of analysis of images, augmented reality, machine vision, and many …

Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

U-Net has become a cornerstone in various visual applications such as image segmentation
and diffusion probability models. While numerous innovative designs and improvements …

Semantic-based segmentation (Semseg) methods play an essential part in medical imaging
analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is …

Biomedical image analysis is fundamental for biomedical discovery. Holistic image analysis
comprises interdependent subtasks such as segmentation, detection and recognition, which …

In this paper, we proposed an enhanced reptile search algorithm (RSA) for global
optimization and selected optimal thresholding values for multilevel image segmentation …

With the advent of advancements in deep learning approaches, such as deep convolution
neural network, residual neural network, adversarial network; U-Net architectures are most …

The early detection of glaucoma is essential in preventing visual impairment. Artificial
intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective …

Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical
images. They have been used extensively for medical image segmentation as the first and …

Popular semi-supervised medical image segmentation networks often suffer from error
supervision from unlabeled data since they usually use consistency learning under different …