Prescreening Entire Mammograms for Masses with Artificial Neural Networks: Preliminary Results

Barry L. Kalman, William R. Reinus, Stan C. Kwasny, Andrew Laine, Lawrence Kotner

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13 Scopus citations


Rationale and Objectives. The authors evaluated the feasibility of combining wavelet transform and artificial neural network (ANN) technologies to prescreen mammograms for masses. Methods and Materials. Fifty-five mammograms (29 with masses and 26 without) were digitized to 100-mm resolution and processed by using wavelet transformation. These wavelets were subjected to a linear output sequential recursive auto-associative memory ANN and cluster analysis with feature vector formation. These vectors were used in two separate experiments - one with 13 cases and another with seven cases held out in a test set - to train feed-forward ANNs to detect the mammograms with a mass. The experiments were repeated with rerandomization of the data, four and six times, respectively. Results. There was a statistically significant correlation (P < .01) between the network's prediction of a mass and the presence of a mass. With majority voting, the feed-forward ANNs detected masses with 79% sensitivity and 50% specificity. Conclusion. Although preliminary, the combination of wavelet transform and ANN is promising and may provide a viable method to prescreen mammograms for masses with high sensitivity and reasonable specificity.

Original languageEnglish
Pages (from-to)405-414
Number of pages10
JournalAcademic radiology
Issue number6
StatePublished - Jun 1997


  • Breast neoplasms, diagnosis
  • Breast radiography
  • Computers, neural network


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