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Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making
Joint Authors
Burnside, Elizabeth S.
Chen, Qiushi
Ayer, Turgay
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-05-26
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Screening mammography is the most effective means for early detection of breast cancer.
Although general rules for discriminating malignant and benign lesions exist, radiologists are unable to perfectly detect and classify all lesions as malignant and benign, for many reasons which include, but are not limited to, overlap of features that distinguish malignancy, difficulty in estimating disease risk, and variability in recommended management.
When predictive variables are numerous and interact, ad hoc decision making strategies based on experience and memory may lead to systematic errors and variability in practice.
The integration of computer models to help radiologists increase the accuracy of mammography examinations in diagnostic decision making has gained increasing attention in the last two decades.
In this study, we provide an overview of one of the most commonly used models, artificial neural networks (ANNs), in mammography interpretation and diagnostic decision making and discuss important features in mammography interpretation.
We conclude by discussing several common limitations of existing research on ANN-based detection and diagnostic models and provide possible future research directions.
American Psychological Association (APA)
Ayer, Turgay& Chen, Qiushi& Burnside, Elizabeth S.. 2013. Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making. Computational and Mathematical Methods in Medicine،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-501754
Modern Language Association (MLA)
Ayer, Turgay…[et al.]. Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making. Computational and Mathematical Methods in Medicine No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-501754
American Medical Association (AMA)
Ayer, Turgay& Chen, Qiushi& Burnside, Elizabeth S.. Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making. Computational and Mathematical Methods in Medicine. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-501754
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-501754