Neural network ensemble-based computer-aided diagnosis for differentiation of lung nodules on CT images: clinical evaluation.

@article{Chen2010NeuralNE,
  title={Neural network ensemble-based computer-aided diagnosis for differentiation of lung nodules on CT images: clinical evaluation.},
  author={Hui Chen and Y Xu and Yujing Ma and Binrong Ma},
  journal={Academic radiology},
  year={2010},
  volume={17 5},
  pages={
          595-602
        }
}
RATIONALE AND OBJECTIVES To evaluate the diagnostic performance of a neural network ensemble-based computer-aided diagnosis (CAD) scheme for classifying lung nodules on thin-section computed tomography (CT). MATERIALS AND METHODS Thirty-two CT images that depicted 19 malignant nodules and 13 benign nodules were used. One of three possible classifications (probably benign, uncertain, and probably malignant) for each nodule was determined by using a neural network ensemble-based CAD scheme. The… CONTINUE READING
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References

Publications referenced by this paper.
SHOWING 1-10 OF 44 REFERENCES

Computer - aided diagnosis for the detection and classification of lung cancers on chest radiographs : ROC analysis of radiologists ’ performance

K Awai
  • Acad Radiol
  • 2006

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