Discrimination of lung cancer and adjacent normal tissues based on permittivity by optimized probabilistic neural network.
10.12122/j.issn.1673-4254.2020.10.17
- Author:
Hongfeng YU
1
;
Ying SUN
1
;
Di LU
2
;
Kaican CAI
2
;
Xuefei YU
1
Author Information
1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
2. Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.
- Publication Type:Journal Article
- Keywords:
lung cancer;
permittivity;
probabilistic neural network;
simulated annealing algorithm
- MeSH:
Algorithms;
Humans;
Lung Neoplasms/diagnosis*;
Neural Networks, Computer;
Sensitivity and Specificity
- From:
Journal of Southern Medical University
2020;40(10):1500-1506
- CountryChina
- Language:Chinese
-
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.
