Hybrid methods for automated diagnosis of breast tumors

Casey Diekman, Wei He, Nagabhushana Prabhu, Harvey Cramer

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


OBJECTIVE: To design and analyze a new family of hybrid methods for the diagnosis of breast tumors using fine needle aspirates. STUDY DESIGN: We present a radically new approach to the design of diagnosis systems. In the new approach, a nonlinear classifier with high sensitivity but low specificity is hybridized with a linear classifier having low sensitivity but high specificity. Data from the Wisconsin Breast Cancer Database are used to evaluate, computationally, the performance of the hybrid classifiers. RESULTS: The diagnosis scheme obtained by hybridizing the nonlinear classifier ellipsoidal multisurface method (EMSM) with the linear classifier proximal support vector machine (PSVM) was found to have a mean sensitivity of 97.36% and a mean specificity of 95.14% and was found to yield a 2.44% improvement in the reliability of positive diagnosis over that of EMSM at the expense of 0.4% degradation in the reliability of negative diagnosis, again compared to EMSM. At the 95% confidence level we can trust the hybrid method to be 96.19-98.53% correct in its malignant diagnosis of new tumors and 93.57-96.71% correct in its benign diagnosis. CONCLUSION: Hybrid diagnosis schemes represent a significant paradigm shift and provide a promising new technique to improve the specificity of nonlinear classifiers without seriously affecting the high sensitivity of nonlinear classifiers.

Original languageEnglish (US)
Pages (from-to)183-190
Number of pages8
JournalAnalytical and Quantitative Cytology and Histology
Issue number4
StatePublished - Aug 2003
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Anatomy
  • Histology


  • Aspiration biopsy
  • Breast cancer
  • Computer assisted
  • Diagnosis


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