Neural vs statistical classifier in conjunction with genetic algorithm feature selection in digital mammographyPaper presented at the IEEE 2003 Congress on Evolutionary Computation (CEC '03)
Date of this Version12-12-2003
Document TypeConference Paper
AbstractDigital mammography is one of the most suitable methods for early detection of breast cancer. It uses digital mammograms to find suspicious areas containing benign and malignant microcalcifications. However, it is very difficult to distinguish benign and malignant microcalcifications. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based feature selection and classification system can provide a second opinion to the radiologists in assessment of microcalcifications. The research proposes and investigates a neural-genetic algorithm for feature selection in conjunction with neural and statistical classifiers to classify microcalcification patterns in digital mammograms. The obtained results show that the proposed approach is able to find an appropriate feature subset and neural classifier achieves better results than two statistical models.
Citation InformationPing Zhang, Brijesh Verma and Kuldeep Kumar. "Neural vs statistical classifier in conjunction with genetic algorithm feature selection in digital mammography" Paper presented at the IEEE 2003 Congress on Evolutionary Computation (CEC '03) (2003)
Available at: http://works.bepress.com/kuldeep_kumar/22/