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

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

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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 Region-based 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.

Colonic polypsDeep learningImproved Faster R-CNNObject detection

LI Jiangyun、ZHANG Jie、CHANG Dedan、HU Yaojun

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School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China

Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China

Department of Gastroenterology, Fu Xing Hospital, Capital Medical University, Beijing 100038, China

This work is supported by the Fundamental Research Funds for the China Central Universities of USTBThis work is supported by the Fundamental Research Funds for the China Central Universities of USTBOpen Project Program of the National Laboratory of Pattern Recognition

FRF-BR-17-004AFRF-GF-17-B49201800027

2019

10.1049/cje.2019.03.005

中国电子杂志(英文版)

中国电子杂志(英文版)

CSTPCDCSCDSCIEI
ISSN：1022-4653
年,卷(期)：2019.28(4)
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