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

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

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

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
Volume4
Issue number6
DOIs
StatePublished - Jun 1997

Keywords

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

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