Performance Comparison of Deep Learning Architectures for Artifact Removal in Gastrointestinal Endoscopic Imaging

Taira Watanabe; Kensuke Tanioka; Satoru Hiwa; Tomoyuki Hiroyasu

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arXiv · 2201.00084

Performance Comparison of Deep Learning Architectures for Artifact Removal in Gastrointestinal Endoscopic Imaging

Taira Watanabe·Kensuke Tanioka·Satoru Hiwa·Tomoyuki Hiroyasu

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Abstract

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 removes 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.

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Taira Watanabe, Kensuke Tanioka, Satoru Hiwa, Tomoyuki Hiroyasu. 2022-01-01. Performance Comparison of Deep Learning Architectures for Artifact Removal in Gastrointestinal Endoscopic Imaging. https://arxiv.org/abs/2201.00084

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