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L. Arbach, L. Bennett, J. M. Reinhardt, and G. Fallouh. Breast mass classification: Comparison between human readers and a back-propagation neural network. In M. Sonka and J. M. Fitzpatrick, eds., Proc. SPIE Conf. Medical Imaging, vol. 5032, pp. 810-818, San Diego, CA, 2003.

Abstract: PURPOSE: We compare mammographic mass classification performance between a backpropagation neural network (BNN), expert radiologists, and residents. Our goal is to reduce false negatives during routine reading of mammograms. METHODS: 160 cases from 3 different institutions were used. Each case contained at least one mass and had an accompanying biopsy result. Masses were extracted using region growing with seed locations identified by an expert radiologist. 10 texture and shape based features (area, perimeter, compactness, radial length, spiculation, mean/standard deviation of radial length, minimum/maximum axis, and boundary roughness) were used as inputs to a three-layer BNN. Shape features were computed on the boundary of the mass region; texture features were computed from the pixel values inside the mass. 140 cases were used for training the BNN and the remaining 20 cases were used for testing. The testing set was diagnosed by three expert radiologists, three residents, and the BNN. We evaluated the human readers and the BNN by computing the area under the ROC curve (AUC). RESULTS: The AUC was 0.923 for the BNN, 0.846 for the expert radiologists, and 0.648 for the residents. These results illustrate the promise of using BNN as a physician?s assistant for breast mass classification.

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Keywords: breast cad

Other publications by: L. Arbach, L. Bennett, J. M. Reinhardt, G. Fallouh

Related journal papers:

L. Arbach, G. Fallouh, J. M. Reinhardt, and L. Bennett. Distinguishing between malignant and nonmalignant breast masses from mammograms using artificial intelligence techniques. Bassel Al-Assad J. for Engineering Science, vol. 16, pp. 103-121, 2002. (details)
L. A. Meinel, A. H. Stolpen, K. S. Berbaum, L. L. Fajardo, and J. M. Reinhardt. Breast MRI Lesion Classification: Improved Performance of Human Readers with a Backpropagation Neural Network Computer-Aided Diagnosis (CAD) System. J. Magn. Res. Imaging, vol. 25, no. 1, pp. 85-95, 2007. (details)

Related conference papers:

L. Arbach, L. Bennett, J. M. Reinhardt, and G. Fallouh. Mammogram Breast Mass Classification with Backpropagation Neural Network. In IEEE Canadian Conference on Electrical and Computer Engineering, vol. 3, pp. 1441-1444, 2003. (details)
L. Arbach, A. H. Stolpen, and J. M. Reinhardt. Classification of breast MRI lesions using a backpropagation neural network (BNN). In 2004 International Symposium on Biomedical Imaging, pp. 253-256, 2004. (details)
M. Sonka, J. Tschirren, S. Ukil, X. Zhang, Y. Xu, J. M. Reinhardt, E. van Beek, G. McLennan, and E. A. Hoffman. Pulmonary CT image analysis and computer aided detection. In Biomedical Imaging: From Nano to Macro, ISBI 2007. 4th IEEE International Symposium on Biomedical Imaging, pp. 500-503, 2007. (details)

Related theses:

L. Arbach. Development of a computer-aided diagnostic system for breast MRI lesion classification. PhD thesis, The University of Iowa, Iowa City, IA, 2005. (details)