ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2020
[Discrimination of lung cancer and adjacent normal tissues based on permittivity by optimized probabilistic neural network].
Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- [Comparison of dielectric properties of normal human esophagus and esophageal cancer using an open-ended coaxial probe].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021Article
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5 authors.
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Abstract
objectiveTo 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.
methodsThe 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.
resultsThree 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%.
conclusionsCompared 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.
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