The Random Subspace Method for Constructing Decision Forests
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Artificial Intelligence in Medicine
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Impact of multiple clusters on neural classification of ROIs in digital mammograms
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Investigating a novel GA-based feature selection method using improved KNN classifiers
International Journal of Information and Communication Technology
Soft computing decision support for a steel sheet incremental cold shaping process
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Semivariogram applied for classification of benign and malignant tissues in mammography
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Customer churn prediction by hybrid model
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Presenting a simplified assistant tool for breast cancer diagnosis in mammography to radiologists
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
International Journal of Data Warehousing and Mining
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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.