Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection

Ping Zhang*, Brijesh Verma, Kuldeep Kumar

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

98 Citations (Scopus)


Digital 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 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.

Original languageEnglish
Pages (from-to)909-919
Number of pages11
JournalPattern Recognition Letters
Issue number7
Publication statusPublished - 15 May 2005


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