Abstract: Objective To propose a probabilistic neural network classification method optimized by simulated annealing algorithm (SA-PNN) to discriminate lung cancer and adjacent normal tissues based on permittivity. Methods The permittivity of lung tumors and the adjacent normal tissues was measured by an open-ended coaxial probe, and the statistical dependency (SD) algorithm was used for frequency screening. The permittivity associated with the selected frequency points was taken as the characteristic variable, and SA-PNN was used to discriminate lung cancer and the adjacent normal tissues. Results Three frequency points, namely 984 MHz, 2724 MHz and 2723 MHz, were selected by SD algorithm. SA-PNN was used to discriminate 200 samples with the permittivity at the 3 frequency points as the characteristic variable. After 10-fold cross-validation, the final discrimination accuracy was 92.50% , the sensitivity was 90.65% , and the specificity was 94.62%. Conclusion Compared with the traditional probabilistic neural network, BP neural network, RBF neural network and the classification discriminant analysis function (Classify) in MATLAB, the proposed SA-PNN has higher accuracy, sensitivity and specificity for discriminating lung cancer and the adjacent normal tissues based on permittivity.

Key words: permittivity, lung cancer, simulated annealing algorithm, probabilistic neural network