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

An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN