12 May 1995 Neural network performance for binary discrimination tasks. Part II: effect of task, training, and feature preselection
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Abstract
Neural networks are applied to the Rayleigh discrimination task. Network performance is compared to results obtained previously using human viewers, and to the best machine approximation to the ideal observer found in an earlier investigation. We find that simple preprocessing of the input image, in this case by projection, greatly improves network convergence and only results obtained on projections are presented here. It is shown that back propagation neural networks significantly outperform a standard nonadaptive linear machine also operating on the projections. In addition, this back propagation neural network performs competitively to a nonadaptive machine that uses the complete two-dimensional information, even though some relevant information is destroyed in the projection process. Finally, improved performance on this Rayleigh task is found for nonlinear (over linear) neural network decision strategies.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kyle J. Myers, Mary P. Anderson, David G. Brown, Robert F. Wagner, Kenneth M. Hanson, "Neural network performance for binary discrimination tasks. Part II: effect of task, training, and feature preselection", Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); doi: 10.1117/12.208757; https://doi.org/10.1117/12.208757
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