Computational and Mathematical Methods in Medicine

Machine Learning-Based Techniques for Automatic Image Quality Enhancement and Pathology Detection in Capsule Endoscopy


Publishing date
01 Mar 2021
Status
Closed
Submission deadline
06 Nov 2020

Lead Editor

1Norwegian University of Science and Technology, Gjovik, Norway

2Tilburg University, Tilburg, Netherlands

3Skåne University Hospital, Malmö, Sweden

4The Royal Infirmary of Edinburgh, Edinburgh, UK

This issue is now closed for submissions.
More articles will be published in the near future.

Machine Learning-Based Techniques for Automatic Image Quality Enhancement and Pathology Detection in Capsule Endoscopy

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Until now, capsule endoscopy has demonstrated greater compliance than traditional endoscopic procedures in the screening of bowel pathologies and diseases since its advent in 2001. Although the most significant milestone remains automatic pathology evaluation and localization, a key step towards achieving that final goal remains the optimization of automatic image quality enhancement and pathology detection techniques.

This Special Issue aims to collate original research contributions that propose new - or optimize existing - automatic image quality enhancement and pathology detection techniques in capsule endoscopy. Machine learning-based techniques for the optimization of automatic capsule endoscopy, image quality enhancement, and pathology detection are particularly welcomed. Review articles that describe the current state of the art of machine learning-based techniques for automatic pathology evaluation and localization in capsule endoscopy are also welcomed.

Potential topics include but are not limited to the following:

  • Machine learning-based optimization of automatic pathology detection
  • Machine learning for automatic colour enhancement
  • Interpolation and fractional motion estimation
  • Machine learning for image resolution enhancement
  • Machine learning for automatic image quality optimization
Computational and Mathematical Methods in Medicine
 Journal metrics
Acceptance rate32%
Submission to final decision46 days
Acceptance to publication39 days
CiteScore3.500
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.