Computer Aided Breast Cancer Diagnosis.
Abstract
The long range goal of this project is to improve the accuracy and consistency of breast cancer diagnosis by developing a Computer Aided Diagnosis (CAD) system for early prediction of breast cancer from patients' mammographic findings and medical history. Specifically, this system will predict the malignancy of non-palpable lesions that are examined with diagnostic mammography and are considered for biopsy. The goal is to improve the specificity of diagnosis with little loss of sensitivity thus significantly improving the positive predictive value of breast biopsy. Toward this goal, we have developed an artificial neural network (ANN) to predict biopsy outcome from mammographic and history findings. In the first four years of the grant we have 1) developed a user interface for acquiring mammographic findings, 2) acquired 700 cases using the standardized BI-RADS. reporting system, 3) trained and evaluated several ANN predictive models, 4) conducted a small prospective study, 5) examined the inter-and intra-observer variability of the reporting lexicon, 6) investigated reducing the number of active input features, and 7) examined the sensitivity of the system to the techniques used for sampling the data.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 1998
- Accession Number
- ADA372209
Entities
People
- Carey E. Floyd
Organizations
- Duke University