File(s) not publicly available
Neural vs statistical classifier in conjunction with genetic algorithm based feature selection
journal contribution
posted on 2017-12-06, 00:00 authored by P Zhang, Brijesh Verma, K KumarDigital 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 in this paper proposes and investigates a neural-genetic algorithm for feature selection in conjunction with neural and statistical classifiers to classifymicrocalcification patterns in digitalmammograms. 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 modes.
Funding
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
History
Volume
26Issue
7Start Page
909End Page
919Number of Pages
11eISSN
0167-8655Location
NetherlandsPublisher
ElsevierPublisher DOI
Full Text URL
Language
en-ausPeer Reviewed
- Yes
Open Access
- No
External Author Affiliations
Faculty of Informatics and Communication; School of Information Technology; TBA Research Institute;Era Eligible
- Yes