Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy
- Submitting institution
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Kingston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11-22-1355
- Type
- D - Journal article
- DOI
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10.1016/j.compmedimag.2015.03.003
- Title of journal
- Computerized Medical Imaging and Graphics
- Article number
- -
- First page
- 64
- Volume
- 43
- Issue
- -
- ISSN
- 0895-6111
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- URL
-
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- Supplementary information
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- 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
-
-
- Research group(s)
-
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- Citation count
- 51
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work described in this paper provides a contribution in the area of detection of proliferative diabetic retinopathy in the form of new vessel detection. The genetic algorithm was combined with previous work on dual classification to enable a specific subset of features to be selected at the final stage of the algorithm to optimise results. The performance of the proposed algorithm was compared to its competitors, achieving better performance metrics than most of them and reducing false responses to bright lesions, dark lesions and reflection artefacts.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -