Microwave breast cancer detection via cost-sensitive ensemble classifiers: Phantom and patient investigation
- Submitting institution
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The University of Surrey
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9026088_1
- Type
- D - Journal article
- DOI
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10.1016/j.bspc.2016.09.003
- Title of journal
- Biomedical Signal Processing and Control
- Article number
- -
- First page
- 366
- Volume
- 31
- Issue
- 0
- ISSN
- 1746-8094
- Open access status
- Deposit exception
- Month of publication
- -
- Year of publication
- 2016
- 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
- 22
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Microwave breast screening techniques involve no ionization radiation, cause minimal discomfort, and can be fabricated inexpensively. This paper is significant because previous work mostly relied on generating an image that can be interpreted by a clinical expert, whereas this work developed a novel machine learning framework which can directly process microwave data for breast cancer detection with low false alarm rate and minimal parameter tuning. The proposed algorithms in this paper hence provide a new direction in developing microwave breast cancer screening systems suitable for large-scale clinical trials.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -