  Polyps are the predecessors to colorectal cancer which is considered as one
of the leading causes of cancer-related deaths worldwide. Colonoscopy is the
standard procedure for the identification, localization, and removal of
colorectal polyps. Due to variability in shape, size, and surrounding tissue
similarity, colorectal polyps are often missed by the clinicians during
colonoscopy. With the use of an automatic, accurate, and fast polyp
segmentation method during the colonoscopy, many colorectal polyps can be
easily detected and removed. The ``Medico automatic polyp segmentation
challenge'' provides an opportunity to study polyp segmentation and build an
efficient and accurate segmentation algorithm. We use the U-Net with
pre-trained ResNet50 as the encoder for the polyp segmentation. The model is
trained on Kvasir-SEG dataset provided for the challenge and tested on the
organizer's dataset and achieves a dice coefficient of 0.8154, Jaccard of
0.7396, recall of 0.8533, precision of 0.8532, accuracy of 0.9506, and F2 score
of 0.8272, demonstrating the generalization ability of our model.
