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Abstract
Context and background
Breast cancer is one of the most common diseases threatening the human lives globally, requiring effective and early risk analysis for which learning classifiers supported with automated feature selection offer a potential robust solution.
Motivation
Computer aided risk analysis of breast cancer typically works with a set of extracted mammographic features which may contain significant redundancy and noise, thereby requiring technical developments to improve runtime performance in both computational efficiency and classification accuracy.
Hypothesis
Use of advanced feature selection methods based on multiple diagnosis criteria may lead to improved results for mammographic risk analysis.
Methods
An approach for multi-criterion based mammographic risk analysis is proposed, by adapting the recently developed multi-label fuzzy-rough feature selection mechanism.
Results
A system for multi-criterion mammographic risk analysis is implemented with the aid of multi-label fuzzy-rough feature selection and its performance is positively verified experimentally, in comparison with representative popular mechanisms.
Conclusions
The novel approach for mammographic risk analysis based on multiple criteria helps improve classification accuracy using selected informative features, without suffering from the redundancy caused by such complex criteria, with the implemented system demonstrating practical efficacy.
Breast cancer is one of the most common diseases threatening the human lives globally, requiring effective and early risk analysis for which learning classifiers supported with automated feature selection offer a potential robust solution.
Motivation
Computer aided risk analysis of breast cancer typically works with a set of extracted mammographic features which may contain significant redundancy and noise, thereby requiring technical developments to improve runtime performance in both computational efficiency and classification accuracy.
Hypothesis
Use of advanced feature selection methods based on multiple diagnosis criteria may lead to improved results for mammographic risk analysis.
Methods
An approach for multi-criterion based mammographic risk analysis is proposed, by adapting the recently developed multi-label fuzzy-rough feature selection mechanism.
Results
A system for multi-criterion mammographic risk analysis is implemented with the aid of multi-label fuzzy-rough feature selection and its performance is positively verified experimentally, in comparison with representative popular mechanisms.
Conclusions
The novel approach for mammographic risk analysis based on multiple criteria helps improve classification accuracy using selected informative features, without suffering from the redundancy caused by such complex criteria, with the implemented system demonstrating practical efficacy.
| Original language | English |
|---|---|
| Article number | 101722 |
| Journal | Artificial Intelligence in Medicine |
| Volume | 100 |
| DOIs | |
| Publication status | Published - 25 Sept 2019 |
Keywords
- learning classifiers
- feature selection
- multi criteria
- multiple labels
- fuzzy-rough dependency
- mammographic risk
- Feature selection
- Multiple labels
- Multiple criteria
- Fuzzy-rough dependency
- Learning classifiers
- Mammographic risk
- Humans
- Machine Learning
- Mammography/methods
- Diagnosis, Computer-Assisted/methods
- Breast Neoplasms/diagnosis
- Algorithms
- Female
- Image Interpretation, Computer-Assisted/methods
- Risk Assessment/methods
- Fuzzy Logic
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- 1 Finished
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Translating uncertainty computing techniques to computer aided diagnosis
Zwiggelaar, R. (PI)
31 Mar 2017 → 30 Mar 2019
Project: Externally funded research