Computer-Assisted Detection of Colonic Polyps Using Improved Faster R-CNN

查看全文 作　　者：LI [1,2]Jiangyun;ZHANG [1,2]Jie;CHANG [1,2]Dedan;HU [3]Yaojun

机构地区：[1]School of Au to mation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China;[2]Key Laboratory of Knowledge Automation for Industrial Processes,Ministry of Education,Beijing 100083,China;[3]Department of Gastroenterology,Fu Xing Hospital,Capital Medical University,Beijing 100038,China

出　　处：《Chinese Journal of Electronics》索引2019年第28卷第4期,共7页

基　　金：supported by the Fundamental Research Funds for the China Central Universities of USTB(No.FRF-BR-17-004A,No.FRF-GF-17-B49);the Open Project Program of the National Laboratory of Pattern Recognition(No.201800027) 摘　　要：The deficiencies of existing polyp detection methods remain:i)They primarily depend on the manually extracted features and require considerable amounts of preprocessing.ii)Most traditional methods cannot specify the location of the polyps in colonoscopy images,especially for the polyps with variable size.In order to derive the improvement and lift the accuracy,we propose a novel and scalable detection algorithm based on deep neural networks-an improved Faster Regionbased Convolutional neural networks(Faster R-CNN)-by increasing the fusion of feature maps at different levels.It can be employed to detect and locate polyps,and even achieve a multi-object task for polyps in the future.The experimental consequences demonstrate that the best version among improved algorithms achieves 97.13%accuracy on the CVC-ClinicDB database,overtaking the previous methods. 关键词：Colonic POLYPS Deep learning IMPROVED FASTER R-CNN Object DETECTION

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Computer-Assisted Detection of Colonic Polyps Using Improved Faster R-CNN

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