  Automatic detection of colonic polyps is still an unsolved problem due to the
large variation of polyps in terms of shape, texture, size, and color, and the
existence of various polyp-like mimics during colonoscopy. In this study, we
apply a recent region based convolutional neural network (CNN) approach for the
automatic detection of polyps in images and videos obtained from colonoscopy
examinations. We use a deep-CNN model (Inception Resnet) as a transfer learning
scheme in the detection system. To overcome the polyp detection obstacles and
the small number of polyp images, we examine image augmentation strategies for
training deep networks. We further propose two efficient post-learning methods
such as, automatic false positive learning and off-line learning, both of which
can be incorporated with the region based detection system for reliable polyp
detection. Using the large size of colonoscopy databases, experimental results
demonstrate that the suggested detection systems show better performance
compared to other systems in the literature. Furthermore, we show improved
detection performance using the proposed post-learning schemes for colonoscopy
videos.
