  KiTs19 challenge paves the way to haste the improvement of solid kidney tumor
semantic segmentation methodologies. Accurate segmentation of kidney tumor in
computer tomography (CT) images is a challenging task due to the non-uniform
motion, similar appearance and various shape. Inspired by this fact, in this
manuscript, we present a novel kidney tumor segmentation method using deep
learning network termed as Hyper vision Net model. All the existing U-net
models are using a modified version of U-net to segment the kidney tumor
region. In the proposed architecture, we introduced supervision layers in the
decoder part, and it refines even minimal regions in the output. A dataset
consists of real arterial phase abdominal CT scans of 300 patients, including
45964 images has been provided from KiTs19 for training and validation of the
proposed model. Compared with the state-of-the-art segmentation methods, the
results demonstrate the superiority of our approach on training dice value
score of 0.9552 and 0.9633 in tumor region and kidney region, respectively.
