Performance Comparison of Deep Learning Architectures for Artifact Removal in Gastrointestinal Endoscopic Imaging. (arXiv:2201.00084v1 [eess.IV])

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 …

arxiv comparison deep learning learning performance