Abstract
Artificial neural networks are being investigated in the field of medical imaging as a means to facilitate pattern recognition and patient classification. In the work reported here, the effects of internal structure and the nature of input data on the performance of neural networks were investigated systematically using computer-simulated data. Network performance was evaluated quantitatively by means of receiver operating characteristic analysis and compared with the performance of an ideal statistical decision maker. We found that the relatively simple neural networks investigated in this study can perform at the level of an ideal decision maker. These simple networks were also found to learn accurately even when the training data are extremely unbalanced with respect to the prevalence of actually positive cases and to differentiate input data patterns by recognizing their unique characteristics.
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Supported by United States Public Health Service grants nos. CA24806 and CA48985 and by an American Cancer Society Junior Faculty Award (JFRA-212).
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Wu, Y., Doi, K., Metz, C.E. et al. Simulation studies of data classification by artificial neural networks: Potential applications in medical imaging and decision making. J Digit Imaging 6, 117–125 (1993). https://doi.org/10.1007/BF03168438
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DOI: https://doi.org/10.1007/BF03168438