Performance comparison of deep learning architectures for surgical instrument image removal in gastrointestinal endoscopic imaging
Performance comparison of deep learning architectures for surgical instrument image removal in gastrointestinal endoscopic imaging
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DOI：
10.1007/s10015-022-00838-8
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发表时间：
2023-01
影响因子：
0.9
通讯作者：
Taira Watanabe;Kensuke Tanioka;S. Hiwa;Tomoyuki Hiroyasu
中科院分区：
文献类型：
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作者：
Taira Watanabe;Kensuke Tanioka;S. Hiwa;Tomoyuki Hiroyasu
内窥镜图像通常包含若干伪影。伪影严重影响计算机辅助诊断中的图像分析结果。卷积神经网络（CNN）是一种深度学习，可以去除这些伪影。已经为CNN提出了各种架构，并且伪影去除的准确性取决于架构的选择而变化。因此，有必要根据所选架构确定伪影去除精度。在本研究中，我们重点关注作为伪影的内窥镜手术器械，并使用七种不同的CNN架构确定和讨论伪影去除准确性。
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.