  Colorectal cancer is among the most prevalent cause of cancer-related
mortality worldwide. Detection and removal of polyps at an early stage can help
reduce mortality and even help in spreading over adjacent organs. Early polyp
detection could save the lives of millions of patients over the world as well
as reduce the clinical burden. However, the detection polyp rate varies
significantly among endoscopists. There is numerous deep learning-based method
proposed, however, most of the studies improve accuracy. Here, we propose a
novel architecture, Residual Upsampling Network (RUPNet) for colon polyp
segmentation that can process in real-time and show high recall and precision.
The proposed architecture, RUPNet, is an encoder-decoder network that consists
of three encoders, three decoder blocks, and some additional upsampling blocks
at the end of the network. With an image size of $512 \times 512$, the proposed
method achieves an excellent real-time operation speed of 152.60 frames per
second with an average dice coefficient of 0.7658, mean intersection of union
of 0.6553, sensitivity of 0.8049, precision of 0.7995, and F2-score of 0.9361.
The results suggest that RUPNet can give real-time feedback while retaining
high accuracy indicating a good benchmark for early polyp detection.
