Reduction of breast biopsies with a modified self-organizing map

Yi Zheng, James F. Greenleaf, John J. Gisvold

Research output: Contribution to journalArticle

45 Scopus citations

Abstract

A modified self-organizing map with nonlinear weight adjustments has been applied to reduce the number of breast biopsies necessary for breast cancer diagnosis. Tissue features representing texture information from digital sonographic breast images were extracted from sonograms of benign and malignant breast tumors. The resulting hyperspace of data points was then used in a modified self-organizing map that objectively segments population distributions of lesions and accurately establishes benign and malignant regions. These methods were applied to a group of 102 problematic breast cases with sonographic images, including 34 with malignant lesions. All lesions were substantiated by excisional biopsy. The system can isolate clusters of purely benign lesions from other clusters containing both benign and malignant lesions. The hybrid neural network defined a region in which about 60% of the benign lesions were located exclusive of any malignant lesions. Using a hybrid approach and leave-one-out method of data evaluation, we estimate that the number of biopsies in this group of women could be decreased by 40-59% with high confidence and that no malignancies were included in the nonbiopsied group. The experimental results also suggest that the modified self-organizing map provides more accurate population distribution maps than conventional Kohonen maps.

Original languageEnglish (US)
Pages (from-to)1386-1396
Number of pages11
JournalIEEE Transactions on Neural Networks
Volume8
Issue number6
DOIs
StatePublished - Dec 1 1997

Keywords

  • Breast biopsy
  • Computer vision radiologist assistant
  • Modified self-organized map
  • Texture analysis
  • Ultrasound image

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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