Abstract
ObjectiveDielectric properties can be used in normal and malignant tissue identification, which requires an effective classifier because of the high throughput nature of the data. With easy training and fast convergence, probabilistic neural networks (PNNs) are widely applied in pattern classification problems. This study aims to propose a classifier to identify metastatic and non-metastatic thoracic lymph nodes in lung cancer patients based on dielectric properties.MethodsThe dielectric properties (permittivity and conductivity) of lymph nodes were measured using an open-ended coaxial probe. The Synthetic Minority Oversampling Technique method was adopted to modify the dataset. Feature parameters were scored to select the appropriate feature vector using a Statistical Dependency algorithm. The dataset was classified using adaptive PNNs with an optimized smooth factor using the simulated annealing PNN (SA-PNN). The results were compared with traditional Probabilistic, Support Vector Machines, k-Nearest Neighbor and the Classify functions in MATLAB.ResultsThe conductivity frequencies of 3959, 3958, 3960, 3978, 3510, 3889, 3888, and 3976 MHz were selected as the feature vectors for 219 lymph nodes (178 non-metastatic and 41 metastatic). Compared with the other methods, SA-PNN achieved the highest classification accuracy (92.92%) and the corresponding specificity and sensitivity were 94.72% and 91.11%, respectively.ConclusionsCompared with the other methods, the SA-PNN proposed in the present study achieved a higher classification accuracy, which provides a new scheme for classification of metastatic and non-metastatic thoracic lymph nodes in lung cancer patients based on dielectric properties.
Highlights
Dielectric properties usually include effective dielectric permittivity and conductivity [1], which are intrinsic properties of biological tissues and can indirectly reflect the physiological state changes of tissues
60% of samples were selected for the training set from the data set of metastatic thoracic lymph nodes and non-metastatic thoracic lymph nodes in lung cancer patients, 20% of samples were selected for the validation set, and 20% of samples were selected for the test set
By combining current and previously published data [19], the dielectric parameters from 41 lung cancer metastatic thoracic lymph nodes and 178 non-metastatic lung thoracic lymph nodes from 74 patients were measured (Table 1) using an open-ended coaxial probe with 3,951 frequency points in the range of 50 MHz to 4 GHz
Summary
Dielectric properties usually include effective dielectric permittivity and conductivity [1], which are intrinsic properties of biological tissues and can indirectly reflect the physiological state changes of tissues. The theoretical basis of the network is Bayesian classification theory and probability density function estimation [6,7,8,9,10]. It can realize the function of nonlinear learning algorithms with linear learning algorithms, which is widely applied in pattern classification problems. Compared with other neural networks, PNN has the advantages of easy training and fast convergence Simulated annealing (SA) algorithm is a general optimization algorithm based on probability It can find the optimal solution of the objective function in a large space. It has the advantages of strong robustness, is suitable for parallel processing, and can be used for the optimization of complex nonlinear problems [11, 12]
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