@Article{cmc.2020.010098,
AUTHOR = {Jingjing Wan, Taiyue Chen, Bolun Chen, Yongtao Yu, Yiyun Sheng, Xinggang Ma},
TITLE = {A Polyp Detection Method Based on FBnet},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {3},
PAGES = {1263--1272},
URL = {http://www.techscience.com/cmc/v63n3/38874},
ISSN = {1546-2226},
ABSTRACT = {The incidence of colorectal cancer (CRC) in China has increased in recent years. 
The mortality rate of CRC has become one of the highest among all cancers; CRC 
increasingly affects the health and quality of people’s lives. However, due to the 
insufficiency of medical resources in China, the workload on medical doctors has further 
increased. In the past few decades, the adult CRC mortality and morbidity rate dropped 
sharply, mainly because of CRC screening and removal of adenomatous polyps. However, 
due to the differences in polyp itself and the skills of endoscopists, the detection rate of 
polyps varies greatly. In this paper, we adopt an anchor-free mechanism and introduce a 
better method to factorize the process of bounding box regression. Firstly, we regress the 
shape of object by the variant of Faster RCNN. Secondly, we re-define the target function 
of the location of object. The experimental result shows that our method achieves a mAP 
of 55.8%, which outperforms other state-of-the-art methods by at least 11.9%. This will 
greatly help to reduce the missed diagnosis of clinicians during endoscopy and treatment, 
and provide effective help for early diagnosis, early treatment and prevention of CRC.},
DOI = {10.32604/cmc.2020.010098}
}