Esophageal Abnormality Detection Using DenseNet Based Faster R-CNN With Gabor Features
- Submitting institution
-
The University of West London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11013
- Type
- D - Journal article
- DOI
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10.1109/ACCESS.2019.2925585
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 84374
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- -
- 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
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work presents a first study of its kind to combine traditional machine learning techniques and more recently developed deep learning methods to propose a novel and improved method of reliably detecting oesophageal abnormalities. The outcome of this research formed a major part of a UKRI Innovation Scholars: Data Science Training in Health & Bioscience research proposal which has been submitted in collaboration with the School of Medicine, Imperial College London, with the outcome awaited at the time of REF submission (contact: Dr. J. Howard, Imperial College).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -