A Likelihood Ratio Classifier for Computer-Aided Diagnosis in Mammography

Abstract

Although screening x-ray mammography has become a very sensitive method for detecting breast cancer, mammography has low specificity in its diagnostic stage. About 67-85% of breast biopsies are performed on benign lesions. Because of cost and detrimental effects of unnecessary biopsies, the number of biopsies performed on benign lesions needs to be reduced. In this research we are developing a highly sensitive and specific computer-aided diagnosis classifier based on the likelihood ratio, which is designed to aid physicians to identify lesions that should not be sent to biopsy.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2004
Accession Number
ADA434330

Entities

People

  • Anna O. Bilska-wolak
  • Carey E. Floyd Jr.

Organizations

  • Duke University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence
  • Breast Cancer
  • Classification
  • Computer Programs
  • Computer-Aided Diagnosis
  • Computers
  • Data Mining
  • Databases
  • Detection
  • Diagnostic Imaging
  • Information Science
  • Machine Learning
  • Medical Personnel
  • Neoplasms
  • Neural Networks
  • Physicians

Fields of Study

  • Medicine

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Medical Imaging.