Computer-Aided Mammography Using Automated Feature Extraction for the Detection and Diagnosis of Breast Cancer.

Abstract

We developed artificial neural network (ANN) techniques to predict breast lesion malignancy based on mammographic features extracted by radiologists. The 3-layer backpropagation ANNs were trained and tested using the round robin technique and evaluated by ROC (receiver operating characteristic) analysis. Using all 11 available features from 206 patients, the ANN performed with ROC area AZ of 0.84 j 0.03, which was not significantly different from the expert radiologists' AZ of 0.85 + or - 0.03 (2-tailed p-value =0.54). We then ranked the importance of individual features to reduce the number of ANN input features. The resulting 6-feature ANN had AZ of 0.86 + or - 0.03 which was still not significantly different than that of the expert radiologists with p =0.34. The result was an optimally simplified ANN for merging features to predict breast lesion malignancy. In the following years, work will focus on automated extraction of those features to feed into the ANN inputs, thus producing a fully automated computer-aided diagnosis system.

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Document Details

Document Type
Technical Report
Publication Date
Oct 12, 1995
Accession Number
ADA302530

Entities

People

  • Joseph Y. Lo

Organizations

  • Duke University Hospital

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Biomedical Research
  • Breast Cancer
  • Cancer
  • Cognition
  • Computer-Aided Diagnosis
  • Computers
  • Detection
  • Extraction
  • Feature Extraction
  • Image Processing
  • Medical Personnel
  • Neoplasms
  • Neural Networks
  • Physicians
  • Standards

Readers

  • Neural Network Machine Learning.
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks