  Colorectal cancer is one of the common cancers in the United States. Polyp is
one of the main causes of the colonic cancer and early detection of polyps will
increase chance of cancer treatments. In this paper, we propose a novel
classification of informative frames based on a convolutional neural network
with binarized weights. The proposed CNN is trained with colonoscopy frames
along with the labels of the frames as input data. We also used binarized
weights and kernels to reduce the size of CNN and make it suitable for
implementation in medical hardware. We evaluate our proposed method using Asu
Mayo Test clinic database, which contains colonoscopy videos of different
patients. Our proposed method reaches a dice score of 71.20% and accuracy of
more than 90% using the mentioned dataset.
