Contour sequence moments for the classification of closed planar shapes
Pattern Recognition
The handbook of brain theory and neural networks
Evolving Neural Networks For The Classification Of Galaxies
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Nonuniform dynamic discretization in hybrid networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
| Hi-index | 0.00 |
The presence of microcalcification clusters in digital mammograms is a primary indicator of early stages of malignant types of breast cancer and its detection is important to prevent the disease. This paper uses a procedure for the classification of microcalcification clusters in mammograms using sequential Difference of Gaussian filters (DoG) and feedforward Neural Networks (NN). Three methods using class separability, forward sequential search and genetic algorithms for feature selection are compared. We found that the use of Genetic Algorithms (GAs) for selecting the features from microcalcifications and microcalcification clusters that will be the inputs of a feedforward Neural Network (NN) results mainly in improvements in overall accuracy, sensitivity and specificity of the classification.