Performance Comparison of Deep Learning Architectures for Artifact Removal in Gastrointestinal Endoscopic Imaging
This work addresses the need for improved image analysis in computer-aided diagnosis for medical professionals, but it is incremental as it compares existing methods without introducing new techniques.
The study tackled the problem of artifact removal in gastrointestinal endoscopic images by comparing the accuracy of seven different CNN architectures, focusing on surgical instruments as artifacts, and reported varying performance metrics.
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.