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Related Experiment Videos

Generating ROC curves for artificial neural networks

K Woods1, K W Bowyer

  • 1Department of Computer Science and Engineering, University of South Florida, Tampa 33620-5399, USA. woods@bigpine.csee.usf.edu

IEEE Transactions on Medical Imaging
|June 1, 1997
PubMed
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This study introduces a novel method for generating receiver operating characteristic (ROC) curves for artificial neural networks (ANNs) in medical imaging. The new technique improves diagnostic performance metrics, offering better area under the ROC curve (AUC) and operating point distribution.

Area of Science:

  • Medical Imaging
  • Machine Learning
  • Biostatistics

Background:

  • Receiver operating characteristic (ROC) analysis is a standard for evaluating diagnostic performance in medical imaging.
  • Artificial neural networks (ANNs) are increasingly used as classifiers in medical imaging, with researchers now reporting ROC curves for them.
  • The conventional method for generating ROC curves with ANNs involves adjusting the output node threshold.

Purpose of the Study:

  • To propose and evaluate a novel technique for generating ROC curves for two-class ANN classifiers.
  • To demonstrate that the proposed method yields superior ROC curves compared to the standard thresholding approach.

Main Methods:

  • Development of a new technique for generating ROC curves specifically for two-class ANN classifiers.

Related Experiment Videos

  • Comparison of the proposed method against the traditional output node thresholding technique.
  • Evaluation of generated ROC curves based on area under the curve (AUC) and operating point distribution.

    • Main Results:

    • The proposed technique generates ROC curves with a greater area under the ROC curve (AUC).
    • The new method results in a more favorable distribution of operating points on the ROC curve.
    • This indicates an improved ability to discern between true positive and false positive rates.

    Conclusions:

    • The novel ROC curve generation technique offers significant advantages for ANN classifiers in medical imaging.
    • This method enhances the assessment of diagnostic performance, leading to more informative ROC analyses.
    • The improved AUC and operating point distribution suggest better clinical utility for ANN-based diagnostic tools.