文献类型：期刊文献

英文题名：A New Approach to Polyp Detection by Pre-Processing of Images and Enhanced Faster R-CNN

作者：Qian, Zhiqin[1];Lv, Yi[1];Lv, Dongyuan[1];Gu, Huijun[1];Wang, Kunyu[1];Zhang, Wenjun[2,3];Gupta, Madan M.[2,3]

机构：[1]East China Univ Sci & Technol, Complex & Intelligent Syst Res Lab CISRL, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[2]Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China;[3]Univ Saskatchewan, Dept Mech Engn, Saskatoon, SK S7N 5A2, Canada

年份：2021

卷号：21

期号：10

起止页码：11374

外文期刊名：IEEE SENSORS JOURNAL

收录：;EI(收录号：20210209736741);WOS:【SCI-EXPANDED(收录号:WOS:000642012400016)】；

基金：The involvement of Wenjun Zhang on this work has been partially supported by SHRF Grant of Canada. This article has also been partially supported by the Program of Production, Research and Development of Minhang District of Shanghai through a funding Grant (Grant No: 2019MHC107) to Zhiqin Qian. The associate editor coordinating the review of this article and approving it for publication was Dr. Julio C. Rodriguez-Quinonez.

语种：英文

外文关键词：Reflection; Image color analysis; Colonoscopy; Cancer; Feature extraction; Colonic polyps; Deep learning; Colonoscopy; image pre-processing; polyp detection; faster region-based convolutional neural network (faster R-CNN)

摘要：Colon cancer is the third most common cancer in the world, and it is increasingly threatening people's health. Early diagnosis is crucial to reducing the threat; however, the chance of missed polyps in today's colonoscopy examination is still high (about 10%) due to limitations in diagnosis techniques and data analysis methods. The colonoscope is a kind of robot and on its tip there is a camera to acquire images. This paper presents a study aimed to improve the rate of successful diagnosis with a new image data analysis approach based on the faster regional convolutional neural network (faster R-CNN). This new approach has two steps for data analysis: (i) pre-processing of images to characterize polyps, and (ii) incorporating of the result of the pre-processing into the faster R-CNN. Specifically, the pre-processing of colonoscopy was expected to reduce the influence of specular reflections, resulting in an improved image, upon which the faster R-CNN algorithm was aplied. There are several improvements of the faster r-CNN tailoring to the task of colon polyps detection. To confirm the superiority of this new approach, the mean average precision (mAP) was used to compare the results obtained with the new approach and the faster R-CNN algorithm. The experimental result shows that the mAP of the new approach is 91.43%, as opposed to 90.57% with the faster R-CNN, which shows a significant improvement.