Discrimination of lung cancer and adjacent normal tissues based on permittivity by optimized probabilistic neural network / 南方医科大学学报

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.

Applicability of SA-SVM classifier to auxiliary diagnosis of renal involvement in SLE / 军事医学

Objective To establish a computer-aided diagnosis (CAD) model for the classification and diagnosis of systemic lupus erythematosus (SLE) complicated with renal involvement,and to provide a new method for the timely detection and diagnosis of the disease.Methods Simulated annealing(SA) algorithm was used to optimize the penalty coefficient C and kernel function parameter g of the support vector machines(SVM) algorithm before an SA-SVM classifier model was established and was applied to the intelligent assistant diagnosis of SLE.Results Unlike the single SVM classifier,this method never fell into local optimum,and improved the classification accuracy of a classifier.The classification accuracy for SLE with renal involvement was as high as 98.72％.Conclusion The experimental results show that this classification model is well applicable to the intelligent diagnosis of SLE with renal involvement.

A clustering method of Chinese medicine prescriptions based on modified firefly algorithm / 中国结合医学杂志

This paper is aimed to study the clustering method for Chinese medicine (CM) medical cases. The traditional K-means clustering algorithm had shortcomings such as dependence of results on the selection of initial value, trapping in local optimum when processing prescriptions form CM medical cases. Therefore, a new clustering method based on the collaboration of firefly algorithm and simulated annealing algorithm was proposed. This algorithm dynamically determined the iteration of firefly algorithm and simulates sampling of annealing algorithm by fitness changes, and increased the diversity of swarm through expansion of the scope of the sudden jump, thereby effectively avoiding premature problem. The results from confirmatory experiments for CM medical cases suggested that, comparing with traditional K-means clustering algorithms, this method was greatly improved in the individual diversity and the obtained clustering results, the computing results from this method had a certain reference value for cluster analysis on CM prescriptions.