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Authors: Pedro Ferreira 1 ; Inês Dutra 1 ; Nuno A. Fonseca 2 ; Ryan Woods 3 and Elizabeth Burnside 4

Affiliations: 1 University of Porto, Portugal ; 2 CRACS-INESC Porto LA, Portugal ; 3 Johns Hopkins Hospital, United States ; 4 University of Wisconsin School of Medicine and Public Health, United States

ISBN: 978-989-8425-34-8

Keyword(s): Mass density, Breast cancer, Mammograms, Classification methods, Data mining, Machine learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Cardiovascular Technologies ; Cloud Computing ; Computing and Telecommunications in Cardiology ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Decision Support Systems ; e-Health ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Health Information Systems ; Medical and Nursing Informatics ; Platforms and Applications ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: Breast screening is the regular examination of a woman’s breasts to find breast cancer in an initial stage. The sole exam approved for this purpose is mammography that, despite the existence of more advanced technologies, is considered the cheapest and most efficient method to detect cancer in a preclinical stage. We investigate, using machine learning techniques, how attributes obtained from mammographies can relate to malignancy. In particular, this study focus is on how mass density can influence malignancy from a data set of 348 patients containing, among other information, results of biopsies. To this end, we applied different learning algorithms on the data set using theWEKA tools, and performed significance tests on the results. The conclusions are threefold: (1) automatic classification of a mammography can reach equal or better results than the ones annotated by specialists, which can help doctors to quickly concentrate on some specific mammogram for a more thorough study; (2 ) mass density seems to be a good indicator of malignancy, as previous studies suggested; (3) we can obtain classifiers that can predict mass density with a quality as good as the specialist blind to biopsy. (More)

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Paper citation in several formats:
Ferreira, P.; Dutra, I.; A. Fonseca, N.; Woods, R. and Burnside, E. (2011). STUDYING THE RELEVANCE OF BREAST IMAGING FEATURES.In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2011) ISBN 978-989-8425-34-8, pages 337-342. DOI: 10.5220/0003172903370342

@conference{healthinf11,
author={Pedro Ferreira. and Inês Dutra. and Nuno A. Fonseca. and Ryan Woods. and Elizabeth Burnside.},
title={STUDYING THE RELEVANCE OF BREAST IMAGING FEATURES},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2011)},
year={2011},
pages={337-342},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003172903370342},
isbn={978-989-8425-34-8},
}

TY - CONF

JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2011)
TI - STUDYING THE RELEVANCE OF BREAST IMAGING FEATURES
SN - 978-989-8425-34-8
AU - Ferreira, P.
AU - Dutra, I.
AU - A. Fonseca, N.
AU - Woods, R.
AU - Burnside, E.
PY - 2011
SP - 337
EP - 342
DO - 10.5220/0003172903370342

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