Intentional versus passive technology use patterns differentially predict attentional control and psychological well-being - Nature

We’re sharing this article early to provide faster access to peer-reviewed, accepted research. It is citable and carries a permanent DOI. This version is subject to further edits and will be replaced automatically by the final Version of Record. All legal disclaimers apply. AbstractLiver tumors are life-threatening diseases that occur due to the rapid growth of liver cells and even result in a chronic condition. Nevertheless, conventional liver tumor detection struggled with the limitations of poor robustness, difficulties in identifying the complex boundaries, and inconsistent segmentation. Hence, the research introduced a hybrid dual attention-based pyramidal deep convolutional network and deep belief network (HDPCBN) for accurate liver tumor detection. Significantly, the HDPCBN approach deployed a pyramidal architecture to learn deep hierarchical features and intricate patterns of the tumor effectively. Moreover, the integration of the dual attention technique gathers the long-range relationships of both spatial and channel dimensions for accurate liver tumor detection.