Artificial neural networks in mammography interpretation and diagnostic decision making

Turgay Ayer, Qiushi Chen, Elizabeth S. Burnside

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


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.

Original languageEnglish (US)
Article number832509
JournalComputational and Mathematical Methods in Medicine
StatePublished - 2013

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • Applied Mathematics


Dive into the research topics of 'Artificial neural networks in mammography interpretation and diagnostic decision making'. Together they form a unique fingerprint.

Cite this