Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 943
- Type
- D - Journal article
- DOI
-
10.3390/s19061265
- Title of journal
- Sensors
- Article number
- 1265
- First page
- -
- Volume
- 19
- Issue
- 6
- ISSN
- 1424-8220
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 29
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The extensive experimentation presented in the paper used medical image datasets to show that CNN architectures deliver superior performance, surpassing traditional machine learning methods by large margins, which supports their effectiveness as automated diagnosis tools. This work resulted in a funded Innovate UK project related to natural language processing and deep learning (KTP 1025201/LivingLens Enterprise Ltd, £213,148, 2018-2020). It was also presented as a funded keynote speech at the (ACM Technically Sponsored) International Conference of Information and Communication Technology, Baghdad, Iraq.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
