In this work, we propose to use deep learning to segment an image based on its color and its content. We start by using the content-color-dependent screening (CCDS) developed previously in [1]. The goal of CCDS is to apply different color assignments for the two or more regular or irregular halftones within the image depending on the local color and content of the image. If the image content contains high variance of color and texture locally, the artifacts due to halftoning will not be as visible as the artifacts in smooth areas of the image [1]. Therefore, the goal of CCDS was to detect smooth areas of the image and apply the best color assignments to those areas. In order to detect the smooth areas, the image segmentation algorithm involving the retrieval of the cluster-map and the segmented edge-map was proposed [1]. The main drawback of the proposed approach is that for a given image, the result highly depends on the initial parameters, such as the number of clusters, low and high thresholds for edge detection, bilateral filter parameters and others. In this work, we propose to use the well-known U-net architecture to detect the smooth areas of the image. U-net is a type of a convolutional neural network (CNN) designed for fast, accurate image segmentation, and it is used to predict a label for every single pixel [2]. The architecture of the U-net is suitable for this work because it consists of a contracting path to capture context and a symmetrical expansive path that enables precise localization [2]. We believe that using the U-net to detect smooth areas of the image will greatly improve the current approach and provide better results.