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 and invasion based on mammographic features extracted by radiologists and by computerized image processing techniques. We incorporated the radiologist impression as an input to the malignancy-predicting ANN, which outperformed the radiologists. We developed a semi-automated technique for extracting and characterizing breast mass margins, and incorporated those features into an ANN to predict malignancy. In preparation for developing ANNs for feature extraction, we explored the underlying behavior of the previous ANNs by examining their error surfaces in weight space. Finally we developed a novel ANN which predicts invasion among malignant breast lesions based on BI-RADS mammographic findings and patient age. This ANN performed well with Az of 0.91 + or - 0.03. Together these four studies provided important new information which will be crucial toward developing a complete system for computer-aided diagnosis of breast cancer.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1996
Accession Number
ADA321765

Entities

People

  • Joseph Y. Lo

Organizations

  • Duke University Hospital

Tags

DTIC Thesaurus Topics

  • Breast Cancer
  • Cancer
  • Computer-Aided Diagnosis
  • Computers
  • Detection
  • Extraction
  • Feature Extraction
  • Image Processing
  • Medical Personnel
  • Neoplasms
  • Neural Networks
  • Physicians

Fields of Study

  • Physics

Readers

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

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Space