Evolving artificial neural networks for screening features from mammograms.

Disagreement or inconsistencies in mammographic interpretation motivates utilizing computerized pattern recognition algorithms to aid the assessment of radiographic features. We have studied the potential for using artificial neural networks (ANNs) to analyze interpreted radiographic features from film screen mammograms. Attention was given to 216 cases (mammogram series) that presented suspicious characteristics. The domain expert (Wasson) quantified up to 12 radiographic features for each case based on guidelines from previous literature. Patient age was also included. The existence or absence of malignancy was confirmed in each case via open surgical biopsy (111 malignant, 105 benign). ANNs of various complexity were trained via evolutionary programming to indicate whether or not a malignancy was present given a vector of scored input features in a statistical cross validation procedure. For suspicious masses, the best evolved ANNs generated a mean area under the receiver operating characteristic curve (AZ) of 0.9196 +/- 0.0040 (1 S.E.), with a mean specificity of 0.6269 +/- 0.0272 at 0.95 sensitivity. Results when microcalcifications were included were not quite as good (AZ = 0.8464), however, ANNs with only two hidden nodes performed as well as more complex ANNs and better than ANNs with only one hidden node. The performance of the evolved ANNs was comparable to prior literature, but with an order of magnitude less complexity. The success of small ANNs in diagnosing breast cancer offers the promise that suitable explanations for the ANN's behavior can be induced, leading to a greater acceptance by physicians.

Linear and neural models for classifying breast masses.

Computational methods can be used to provide an initial screening or a second opinion in medical settings and may improve the sensitivity and specificity of diagnoses. In the current study, linear discriminant models and artificial neural networks are trained to detect breast cancer in suspicious masses using radiographic features and patient age. Results on 139 suspicious breast masses (79 malignant, 60 benign, biopsy proven) indicate that a significant probability of detecting malignancies can be achieved at the risk of a small percentage of false positives. Receiver operating characteristic (ROC) analysis favors the use of linear models, however, a new measure related to the area under the ROC curve (AZ) suggests a possible benefit from hybridizing linear and nonlinear classifiers.

A step toward computer-assisted mammography using evolutionary programming and neural networks.

Artificial intelligence techniques can be used to provide a second opinion in medical settings. This may improve the sensitivity and specificity of diagnoses, as well as the cost effectiveness of the physician's effort. In the current study, evolutionary programming is used to train artificial neural networks to detect breast cancer using radiographic features and patient age. Results from 112 suspicious breast masses (63 malignant, 49 benign, biopsy proven) indicate that a significant probability of detecting malignancies can be achieved using simple neural architectures at the risk of a small percentage of false positives.

Evolving neural networks for detecting breast cancer.

Artificial neural networks are applied to the problem of detecting breast cancer from histologic data. Evolutionary programming is used to train the networks. This stochastic optimization method reduces the chance of becoming trapped in locally optimal weight sets. Preliminary results indicate that very parsimonious neural nets can outperform other methods reported in the literature on the same data. The results are statistically significant.