mlsanityData to 14 Oct 2026

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

arXiv 2201.00084 ↗First posted 1 Jan 2022eess.IV cs.CV cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 23 references that are in the map. First posted on arXiv 1 Jan 2022.

Builds on

16 of 23 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  4. 2016Densely Connected Convolutional Networkscited by 6,720
  5. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  6. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  7. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
  8. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  9. 2017Squeeze-and-Excitation Networkscited by 4,789
  10. 2014Going Deeper with Convolutionscited by 4,241
  11. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  12. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,512
4 more
  1. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,487
  2. 2020DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentationcited by 125
  3. 2018TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentationcited by 76
  4. 2020Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopycited by 54

Built on by

Not cited yet.