Back to Search
Start Over
A Comparison of Feature Selection Methods for the Detection of Breast Cancers in Mammograms: Adaptive Sequential Floating Search vs. Genetic Algorithm
- Source :
- 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.
- Publication Year :
- 2005
- Publisher :
- IEEE, 2005.
-
Abstract
- This paper presents a comparison of feature selection methods for a unified detection of breast cancers in mammograms. A set of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, were extracted from a region of 512×512 pixels containing normal tissue or breast cancer. Adaptive floating search and genetic algorithm were used for the feature selection, and a linear discriminant analysis (LDA) was used for the classification of cancer regions from normal regions. The performance is evaluated using A z , the area under ROC curve. On a dataset consisting 296 normal regions and 164 cancer regions (53 masses, 56 spiculated lesions, and 55 calcifications), adaptive floating search achieved A z =0.96 with comparison to A z =0.93 of CHC genetic algorithm and A z =0.90 of simple genetic algorithm.
- Subjects :
- medicine.diagnostic_test
Contextual image classification
Computer science
business.industry
Feature extraction
Cancer
Pattern recognition
Feature selection
Linear discriminant analysis
medicine.disease
Breast cancer
Image texture
Genetic algorithm
medicine
Mammography
Computer vision
Artificial intelligence
business
Image resolution
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference
- Accession number :
- edsair.doi.dedup.....180fda33a406e67e9f1aaf88752a4c46