UNet Based

UNet-based architectures are a cornerstone of image segmentation, particularly in medical imaging and remote sensing, aiming to accurately delineate objects within images. Current research focuses on enhancing UNet's performance through modifications like incorporating transformer networks for improved long-range dependency modeling, lightweight designs for resource-constrained applications, and the integration of attention mechanisms and multi-scale feature fusion. These advancements significantly improve segmentation accuracy and efficiency across diverse applications, impacting fields ranging from medical diagnosis and treatment planning to automated cell analysis and environmental monitoring.