Effective recognition of MCCs in mammograms using an improved neural classifier

J. C. Ren, D. Wang, J. M. Jiang

Research output: Contribution to journal › Article

30 Citations (Scopus)

Abstract

Computer-aided diagnosis is one of the most important engineering applications of artificial intelligence. In this paper, early detection of breast cancer through classification of microcalcification clusters from mammograms is emphasized. Although artificial neural network (ANN) has been widely applied in this area, the average accuracy achieved is only around 80% in terms of the area under the receiver operating characteristic curve A(z). This performance may become much worse when the training samples are imbalanced. As a result, an improved neural classifier is proposed, in which balanced learning with optimized decision making are introduced to enable effective learning from imbalanced samples. When the proposed learning strategy is applied to individual classifiers, the results on the DDSM database have demonstrated that the performance from has been significantly improved. An average improvement of more than 10% in the measurements of F(1) score and A(z) has fully validated the effectiveness of our proposed method for the successful classification of clustered microcalcifications.
Original languageEnglish
Pages (from-to)638-645
Number of pages8
JournalEngineering Applications of Artificial Intelligence
Volume24
Issue number4
DOIs
Publication statusPublished - Jun 2011

Fingerprint

Classifiers
Computer aided diagnosis
Artificial intelligence
Decision making
Neural networks

Keywords

  • optimized decision making
  • breast-cancer
  • mammography
  • IMBA
  • digital mammography
  • artificial neural network
  • feature-extraction
  • image retrieval
  • clustered microcalcifications
  • balanced learning
  • microcalcification clusters (MCC)
  • computer-aided diagnosis
  • effective recognition
  • mcc's
  • mammograms
  • improved
  • neural classifier

Cite this

Ren, J. C. ; Wang, D. ; Jiang, J. M. / Effective recognition of MCCs in mammograms using an improved neural classifier. In: Engineering Applications of Artificial Intelligence . 2011 ; Vol. 24, No. 4. pp. 638-645.
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abstract = "Computer-aided diagnosis is one of the most important engineering applications of artificial intelligence. In this paper, early detection of breast cancer through classification of microcalcification clusters from mammograms is emphasized. Although artificial neural network (ANN) has been widely applied in this area, the average accuracy achieved is only around 80{\%} in terms of the area under the receiver operating characteristic curve A(z). This performance may become much worse when the training samples are imbalanced. As a result, an improved neural classifier is proposed, in which balanced learning with optimized decision making are introduced to enable effective learning from imbalanced samples. When the proposed learning strategy is applied to individual classifiers, the results on the DDSM database have demonstrated that the performance from has been significantly improved. An average improvement of more than 10{\%} in the measurements of F(1) score and A(z) has fully validated the effectiveness of our proposed method for the successful classification of clustered microcalcifications.",
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Ren, JC, Wang, D & Jiang, JM 2011, 'Effective recognition of MCCs in mammograms using an improved neural classifier', Engineering Applications of Artificial Intelligence , vol. 24, no. 4, pp. 638-645. https://doi.org/10.1016/j.engappai.2011.02.011

Effective recognition of MCCs in mammograms using an improved neural classifier. / Ren, J. C.; Wang, D.; Jiang, J. M.

In: Engineering Applications of Artificial Intelligence , Vol. 24, No. 4, 06.2011, p. 638-645.

Research output: Contribution to journal › Article

TY - JOUR

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Ren JC, Wang D, Jiang JM. Effective recognition of MCCs in mammograms using an improved neural classifier. Engineering Applications of Artificial Intelligence . 2011 Jun;24(4):638-645. https://doi.org/10.1016/j.engappai.2011.02.011

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