  Accurate computer-aided polyp detection and segmentation during colonoscopy
examinations can help endoscopists resect abnormal tissue and thereby decrease
chances of polyps growing into cancer. Towards developing a fully automated
model for pixel-wise polyp segmentation, we propose ResUNet++, which is an
improved ResUNet architecture for colonoscopic image segmentation. Our
experimental evaluations show that the suggested architecture produces good
segmentation results on publicly available datasets. Furthermore, ResUNet++
significantly outperforms U-Net and ResUNet, two key state-of-the-art deep
learning architectures, by achieving high evaluation scores with a dice
coefficient of 81.33%, and a mean Intersection over Union (mIoU) of 79.27% for
the Kvasir-SEG dataset and a dice coefficient of 79.55%, and a mIoU of 79.62%
with CVC-612 dataset.
