Performance comparison of deep learning architectures for surgical instrument image removal in gastrointestinal endoscopic imaging

被引：0
|
作者：
Watanabe, Taira [1 ]
Tanioka, Kensuke [1 ]
Hiwa, Satoru [1 ]
Hiroyasu, Tomoyuki [2 ]
机构：
[1] Doshisha Univ, Dept Biomed Sci & Informat, Kyoto, Japan
[2] Doshisha Univ, Grad Sch Life & Med Sci, Kyoto, Japan
来源：
ARTIFICIAL LIFE AND ROBOTICS | 2023年 / 28卷 / 02期
关键词：
Deep learning; Convolutional neural networks; Gastrointestinal endoscopic images; Semantic segmentation; Artifact removal method;
D O I：
10.1007/s10015-022-00838-8
中图分类号：
TP24 [机器人技术];
学科分类号：
080202 ; 1405 ;
摘要：
Endoscopic images typically contain several artifacts. The artifacts significantly impact image analysis result in computer-aided diagnosis. Convolutional neural networks (CNNs), a type of deep learning, can remove such artifacts. Various architectures have been proposed for the CNNs, and the accuracy of artifact removal varies depending on the choice of architecture. Therefore, it is necessary to determine the artifact removal accuracy, depending on the selected architecture. In this study, we focus on endoscopic surgical instruments as artifacts, and determine and discuss the artifact removal accuracy using seven different CNN architectures.
引用
收藏
页码：307 / 313
页数：7
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