A self-attention based faster R-CNN for polyp detection from colonoscopy images

Abstract

At present, the incidence rate of colorectal cancer (CRC) is increasing year by year. It has always affected people's physical and mental health and quality of life. How to improve the detection ability of polyp plays a key role in colonoscopy. In order to solve these problems, in this paper, we first enhance the contrast of the input image by well distinguishing the foreground from the background in order to improve the saliency of the polyp regions. Then, we feed the enhanced image into an improved Faster R-CNN architecture comprised three processing modules for feature extraction, region proposal generation, and polyp detection, respectively. In order to further improve the quality, as well as the feature abstraction capability of the feature maps produced by the feature extraction network, we append an attention module to attend to the useful feature channels and weaken the contributions of the helpless feature channels. The experimental results demonstrate that the accuracy of the proposed polyp detection network is greatly improved compared with the existing algorithms, and the network not only can accurately identify polyps of varying sizes and conditions in single polyp images, but also can achieve excellent performance in handling multiple polyp images. This paper will be greatly helpful to alleviate the missed diagnosis of clinicians in the process of endoscopic examination and disease treatment, as well as providing effective assistance for the early diagnosis, treatment and prevention of the CRC, which is also of great significance to the clinical work of physicians.