Accession Number : ADA321765

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

Descriptive Note : Annual rept. 15 Sep 95-14 Sep 96,

Corporate Author : DUKE UNIV MEDICAL CENTER DURHAM NC

Personal Author(s) : Lo, Joseph Y.

PDF Url : ADA321765

Report Date : OCT 1996

Pagination or Media Count : 19

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.

Descriptors :   *COMPUTER AIDED DIAGNOSIS, *BREAST CANCER, *MAMMOGRAPHY, COMPUTER PROGRAMS, IMAGE PROCESSING, NEURAL NETS, AUTOMATION, DETECTION, INPUT OUTPUT PROCESSING, SURFACES, DIAGNOSIS(MEDICINE), PATTERN RECOGNITION, EXTRACTION, LESIONS, CANCER, MAMMARY GLANDS, RADIOLOGY, TEST SETS.

Subject Categories : Anatomy and Physiology
      Medicine and Medical Research
      Computer Programming and Software
      Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE