U Net Model

U-Net models are a widely used convolutional neural network architecture primarily designed for image segmentation tasks, particularly in medical imaging. Current research focuses on optimizing U-Net performance through architectural modifications (like Attention U-Nets and variations incorporating ResNet or ConvNeXt components), investigating the impact of receptive field size on accuracy and efficiency, and exploring self-supervised learning techniques to reduce reliance on large annotated datasets. These advancements are significantly improving the accuracy and efficiency of automated image segmentation in diverse applications, ranging from brain tumor detection to weather forecasting, ultimately aiding in medical diagnosis and treatment planning.