Investigation of Genetic Algorithms for Computer-Aided Diagnosis.
Abstract
Computer-aided diagnosis has the potential of substantially increasing diagnostic accuracy in mammography. Using a computer to double-check a radiologist's findings is becoming more popular and more important as the public learns that the best defense against breast cancer is early detection. The University of Chicago is currently developing computerized schemes to detect cancers in digital mammograms. We use a pattern recognition system known as an artificial neural network (ANN) to classify certain regions of the digital mammograms as cancerous or non-cancerous. ANNs are trained pattern recognition devices that take, as inputs, features extracted from regions in the mammograms and output the classification. Currently, there are a total of 71 features extracted from the various regions in each mammogram. A subset of those 71 features must be chosen as inputs for the ANN. The goal of the proposed research is to apply a technique known as a genetic algorithm and other optimization techniques to find the subset of features which would result in the best ANN performance. By improving the inputs to the ANN, the performance of the neural network and hence, the performance of the mass CAD scheme, should improve. Preliminary results have exhibited this improvement.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 1998
- Accession Number
- ADA369253
Entities
People
- Matthew A. Kupinski
Organizations
- University of Chicago