Using evolutionary computation to learn about detecting breast cancer

Computer assisted mammography can be used to provide a second opinion and may improve the sensitivity and specificity of diagnosis. Algorithms may also provide a basis for mining data from available training sets, thereby allowing the user to recognize relationships between input features and alternative conditions (e.g., malignant, benign). The paper provides a review of recent efforts to evolve neural networks and linear classifiers to assist in the detection of breast cancer.