Classification of Individual and Clustered Microcalcifications in Digital Mammograms Using Evolutionary Neural Networks

  • Rolando R. Hernández-Cisneros
  • Hugo Terashima-Marín
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)


Breast cancer is one of the main causes of death in women and early diagnosis is an important means to reduce the mortality rate. The presence of microcalcification clusters are primary indicators of early stages of malignant types of breast cancer and its detection is important to prevent the disease. This paper proposes a procedure for the classification of microcalcification clusters in mammograms using sequential difference of gaussian filters (DoG) and three evolutionary artificial neural networks (EANNs) compared against a feedforward artificial neural network (ANN) trained with backpropagation. We found that the use of genetic algorithms (GAs) for finding the optimal weight set for an ANN, finding an adequate initial weight set before starting a backpropagation training algorithm and designing its architecture and tuning its parameters, results mainly in improvements in overall accuracy, sensitivity and specificity of an ANN, compared with other networks trained with simple backpropagation.


Hide Layer Digital Mammogram Feature Selection Process Evolutionary Neural Network Microcalcification Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Rolando R. Hernández-Cisneros
    • 1
  • Hugo Terashima-Marín
    • 1
  1. 1.Center for Intelligent SystemsTecnológico de MonterreyMonterrey, Nuevo LeónMexico

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