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Residual Network (ResNet-18) for Laparoscopic Image Distortion Classification

EasyChair Preprint no. 11575

4 pagesDate: December 19, 2023


Diminished laparoscopic video quality directly affects a surgeon's visibility and can compromise the outcomes of computational tasks in robot-assisted surgery. To address this challenge, numerous solutions have been proposed based on the detection and classification of laparoscopic video distortions. In this work, we propose a method based on Residual networks (ResNet18) for the automatic detection and classification of noise ‘NO’, smoke ‘SM’, uneven illumination ‘UI’, defocus blur ‘DB’, and motion blur ‘MB’ in laparoscopic videos. We have obtained an accuracy of 98.75% for training and 97.97% for validation. The high accuracy scores across the classes emphasize the model's capability to generalize well and make accurate predictions.

Keyphrases: deep learning, distortion classification, Laparoscopic video

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Mohamed Belmokeddem and Kamila Khemis and Salim Loudjedi},
  title = {Residual Network (ResNet-18) for Laparoscopic Image Distortion Classification},
  howpublished = {EasyChair Preprint no. 11575},

  year = {EasyChair, 2023}}
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