FIG 5. A schematic of a two-hidden-node NN used for the analysis of multiple peak height data. Seven peak heights for a sample serve as input to the nodes of the input layer. The computations propagate through the network, and the classification as tumor or nontumor is read from the nodes of the output layer.FIG 6. ROC curves, computed using the binormal method, for each classifier tested (NN-2=two-node NN, NN-1=one-node NN, LDA-Cho/Cr=LDA for Cho/Cr area ratio, LDA-Heights=LDA for Cho/Cr peak height ratio). TPR=true-positive rate (sensitivity); FPR=false-positive rate (1-specificity). ROC analysis demonstrates that NN analysis outperfoms linear discriminant analysis for measuring the diagnostic accuracy of ¹H-MR in attempting to differentiate SCCA from muscle