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Applying modular classifiers to mammographic mass classification.

V Shah1, L M Bruce, N Younan

  • 1Department of Electronics and Computer Engineering, Mississippi State University, MS 39762, USA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 3, 2007
PubMed
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Modular classifier schemes significantly improve breast mass classification sensitivity in computer-aided diagnosis (CAD) systems. This enhances malignancy detection accuracy from mammograms, aiding radiologists in diagnosis.

Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Machine Learning

Background:

  • Accurate classification of breast masses in digital mammograms is crucial for computer-aided diagnosis (CAD) systems.
  • Improving sensitivity in the classification stage is vital for early and accurate malignancy detection.

Purpose of the Study:

  • To investigate the effectiveness of modular classifier (MoC) schemes, specifically bagging and adaboost, for automated classification of mammographic masses.
  • To compare the performance of MoC-based CAD systems against traditional classifier (TrC) systems.

Main Methods:

  • Utilized 200 digitized mammograms with radiologist-segmented breast masses.
  • Extracted conventional shape-based features from segmented masses.
  • Optimized features using Fischer's linear discriminant analysis (LDA) and compared MoC (bagging, adaboost) with TrC (nearest mean, maximum likelihood).

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Main Results:

  • Without LDA, MoC schemes increased sensitivity from 74% to 83% compared to TrC.
  • After LDA, sensitivity further increased to 88% for both TrC and MoC schemes, with MoC showing a slight advantage.

Conclusions:

  • Modular classifier schemes offer a significant improvement in sensitivity for classifying mammographic masses.
  • Feature optimization using LDA further enhances classification performance in CAD systems.
  • MoC approaches show promise for improving the accuracy of malignancy detection in breast cancer screening.