U-Net Deep Learning Architecture. U-Net is a deep learning architecture…

U-Net is a deep learning architecture used for image segmentation tasks, particularly in medical imaging. It was proposed by Ronneberger et al. in 2015. The U-Net architecture consists of a contracting path and an expanding path. The contracting path is similar to a traditional convolutional neural network (CNN) architecture, where the input image is progressively downsampled to extract high-level features. The expanding path, on the other hand, is designed to recover the spatial resolution of the output segmentation mask by performing a series of upsampling operations.