arXiv Open Access 2022

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

Taira Watanabe Kensuke Tanioka Satoru Hiwa Tomoyuki Hiroyasu

Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

eess.IV cs.CV cs.LG

Penulis (4)

Taira Watanabe

Kensuke Tanioka

Satoru Hiwa

Tomoyuki Hiroyasu

Format Sitasi

APA MLA BibTeX

Watanabe, T., Tanioka, K., Hiwa, S., Hiroyasu, T. (2022). Performance Comparison of Deep Learning Architectures for Artifact Removal in Gastrointestinal Endoscopic Imaging. https://arxiv.org/abs/2201.00084

Akses Cepat

Lihat di Sumber

Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓