  Colorectal cancer is one of fatal cancer worldwide. Colonoscopy is the
standard treatment for examination, localization, and removal of colorectal
polyps. However, it has been shown that the miss-rate of colorectal polyps
during colonoscopy is between 6 to 27%. The use of an automated, accurate, and
real-time polyp segmentation during colonoscopy examinations can help the
clinicians to eliminate missing lesions and prevent further progression of
colorectal cancer. The ``Medico automatic polyp segmentation challenge''
provides an opportunity to study polyp segmentation and build a fast
segmentation model. The challenge organizers provide a Kvasir-SEG dataset to
train the model. Then it is tested on a separate unseen dataset to validate the
efficiency and speed of the segmentation model. The experiments demonstrate
that the model trained on the Kvasir-SEG dataset and tested on an unseen
dataset achieves a dice coefficient of 0.7801, mIoU of 0.6847, recall of
0.8077, and precision of 0.8126, demonstrating the generalization ability of
our model. The model has achieved 80.60 FPS on the unseen dataset with an image
resolution of $512 \times 512$.
