Title :
Using evolutionary computation to learn about detecting breast cancer
Author :
Fogel, D.B. ; Angeline, P.J. ; Porto, V.W. ; Wasson, E.C., III ; Boughton, E.M.
Author_Institution :
Natural Selection Inc., La Jolla, CA, USA
Abstract :
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
Keywords :
data mining; evolutionary computation; learning (artificial intelligence); mammography; medical expert systems; medical image processing; alternative conditions; breast cancer detection; computer assisted mammography; data mining; evolutionary computation; input features; linear classifiers; neural network evolution; training sets; Artificial neural networks; Breast cancer; Cancer detection; Data mining; Evolutionary computation; Hospitals; Laboratories; Mammography; Pattern recognition; Sensitivity and specificity;
Conference_Titel :
Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5536-9
DOI :
10.1109/CEC.1999.785485
