Vol.63, No.3, 2020, pp.1263-1272, doi:10.32604/cmc.2020.010098
A Polyp Detection Method Based on FBnet
  • Jingjing Wan1, Taiyue Chen2, *, Bolun Chen2, 3, *, Yongtao Yu2, Yiyun Sheng2, Xinggang Ma1
1 Department of Gastroenterology, Affiliated Huaian Hospital of Xuzhou Medical University, The Second People’s Hospital of Huaian, Huaian, 223002, China.
2 College of Computer Engineering, Huaiyin Institute of Technology, Huaian, 223003, China.
3 University of Fribourg, Fribourg, 1700, Switzerland.
* Corresponding Authors: Bolun Chen. Email: ;
  Taiyue Chen, Email: .
Received 11 February 2020; Accepted 24 February 2020; Issue published 30 April 2020
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.
Colorectal cancer, polyp detection, anchor free, two step decomposition.
Cite This Article
. , "A polyp detection method based on fbnet," Computers, Materials & Continua, vol. 63, no.3, pp. 1263–1272, 2020.
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