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.
Artículo
en Chino
| WPRIM
| ID: wpr-880770
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.
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Asunto principal:
Algoritmos
/
Sensibilidad y Especificidad
/
Redes Neurales de la Computación
/
Neoplasias Pulmonares
Tipo de estudio:
Estudio diagnóstico
/
Estudio pronóstico
Límite:
Humanos
Idioma:
Chino
Revista:
Journal of Southern Medical University
Año:
2020
Tipo del documento:
Artículo
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