Discrimination of lung cancer and adjacent normal tissues based on permittivity by optimized probabilistic neural network / 南方医科大学学报
Journal of Southern Medical University
; (12): 1500-1506, 2020.
Article
em Zh
| WPRIM
| ID: wpr-880770
Biblioteca responsável:
WPRO
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%.@*CONCLUSIONS@#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.
Palavras-chave
Texto completo:
1
Base de dados:
WPRIM
Assunto principal:
Algoritmos
/
Sensibilidade e Especificidade
/
Redes Neurais de Computação
/
Neoplasias Pulmonares
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
Zh
Revista:
Journal of Southern Medical University
Ano de publicação:
2020
Tipo de documento:
Article