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Computer-aided diagnosis of breast cancer: artificial neural network approach for optimized merging of mammographic features.

Publication ,  Journal Article
Lo, JY; Baker, JA; Kornguth, PJ; Floyd, CE
Published in: Acad Radiol
October 1995

RATIONALE AND OBJECTIVES: An artificial neural network (ANN) approach was developed for the computer-aided diagnosis of mammography using an optimally minimized number of input features. METHODS: A backpropagation ANN merged nine input features (age plus eight radiographic findings extracted by radiologists) to predict biopsy outcome as its output. The features were ranked, and more important ones were selected to produce an optimal subset of features. RESULTS: Given all nine features, the ANN performed with a receiver operator characteristic area under the curve (Az) of .95 +/- .01. Given only the four most important features, the ANN performed with an Az of .96 +/- .01. Although not significantly better than the ANN with all nine features, the ANN with the four optimized features was significantly better than expert radiologists' Az of .90 +/- .02 (p = .01). This four-feature ANN had a 95% sensitivity and an 81% specificity. For cases with calcifications, the radiologists' performance dropped to an Az of .85 +/- .04, whereas a specialized three-feature ANN performed significantly better with an Az of .95 +/- .02 (p = .02). CONCLUSION: Given only four input features, the ANN predicted biopsy outcome significantly better than did expert radiologists, who also had access to other radiographic and nonradiographic data. The reduced number of features would substantially decrease data entry efforts and potentially improve the ANN's general applicability.

Duke Scholars

Published In

Acad Radiol

DOI

ISSN

1076-6332

Publication Date

October 1995

Volume

2

Issue

10

Start / End Page

841 / 850

Location

United States

Related Subject Headings

  • Sensitivity and Specificity
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Mammography
  • Humans
  • Female
  • Diagnosis, Computer-Assisted
  • Breast Neoplasms
  • 3202 Clinical sciences
  • 1103 Clinical Sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Lo, J. Y., Baker, J. A., Kornguth, P. J., & Floyd, C. E. (1995). Computer-aided diagnosis of breast cancer: artificial neural network approach for optimized merging of mammographic features. Acad Radiol, 2(10), 841–850. https://doi.org/10.1016/s1076-6332(05)80057-1
Lo, J. Y., J. A. Baker, P. J. Kornguth, and C. E. Floyd. “Computer-aided diagnosis of breast cancer: artificial neural network approach for optimized merging of mammographic features.Acad Radiol 2, no. 10 (October 1995): 841–50. https://doi.org/10.1016/s1076-6332(05)80057-1.
Lo, J. Y., et al. “Computer-aided diagnosis of breast cancer: artificial neural network approach for optimized merging of mammographic features.Acad Radiol, vol. 2, no. 10, Oct. 1995, pp. 841–50. Pubmed, doi:10.1016/s1076-6332(05)80057-1.

Published In

Acad Radiol

DOI

ISSN

1076-6332

Publication Date

October 1995

Volume

2

Issue

10

Start / End Page

841 / 850

Location

United States

Related Subject Headings

  • Sensitivity and Specificity
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Mammography
  • Humans
  • Female
  • Diagnosis, Computer-Assisted
  • Breast Neoplasms
  • 3202 Clinical sciences
  • 1103 Clinical Sciences