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Mammography Feature Selection using Rough set Theory
- Source :
- 2006 International Conference on Advanced Computing and Communications.
- Publication Year :
- 2006
- Publisher :
- IEEE, 2006.
-
Abstract
- Microcalcification on X-ray mammogram is a significant mark for early detection of breast cancer. Texture analysis methods can be applied to detect clustered microcalcification in digitized mammograms. In order to improve the predictive accuracy of the classifier, the original number of feature set is reduced into smaller set using feature reduction techniques. In this paper rough set based reduction algorithms such as , Quickreduct (QR) and proposes Modified Quickreduct (MQR) are used to reduce the extracted features. The performance of both algorithms is compared. The Gray Level Co-occurrence Matrix (GLCM) is generated for each mammogram to extract the Haralick features as feature set. The reduction algorithms are tested on 161 pairs of digitized mammograms from Mammography Image Analysis Society (MIAS) database.
- Subjects :
- medicine.diagnostic_test
Computer science
business.industry
Feature extraction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Early detection
Cancer
Pattern recognition
Feature selection
medicine.disease
Breast cancer
Image texture
medicine
Mammography
Computer vision
Artificial intelligence
Rough set
Microcalcification
medicine.symptom
skin and connective tissue diseases
business
Classifier (UML)
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2006 International Conference on Advanced Computing and Communications
- Accession number :
- edsair.doi...........8a8431b664d98dc0fac0ae3ea993b90c