  Polyp has long been considered as one of the major etiologies to colorectal
cancer which is a fatal disease around the world, thus early detection and
recognition of polyps plays a crucial role in clinical routines. Accurate
diagnoses of polyps through endoscopes operated by physicians becomes a
challenging task not only due to the varying expertise of physicians, but also
the inherent nature of endoscopic inspections. To facilitate this process,
computer-aid techniques that emphasize fully-conventional image processing and
novel machine learning enhanced approaches have been dedicatedly designed for
polyp detection in endoscopic videos or images. Among all proposed algorithms,
deep learning based methods take the lead in terms of multiple metrics in
evolutions for algorithmic performance. In this work, a highly effective model,
namely the faster region-based convolutional neural network (Faster R-CNN) is
implemented for polyp detection. In comparison with the reported results of the
state-of-the-art approaches on polyps detection, extensive experiments
demonstrate that the Faster R-CNN achieves very competing results, and it is an
efficient approach for clinical practice.
