Application of Probabilistic Neural Networks Method to Gastric Endoscope Samples Diagnosis Based on FTIR Spectroscopy

In the present paper, probabilistic neural network method was applied to the classification of gastric endoscope samples based on FTIR spectroscopy for higher discrimination correctness than the conventional linear discriminant analysis algorithm. The probabilistic neural network (PNN) is a kind of radial basis network suitable for discriminant analysis. There are several advantages of PNN method: less time is needed to train the model, higher correctness could be achieved, global optimal solution could be obtained and so on. In this paper, PNN method was utilized to classify gastric endoscopic biopsies into healthy, gastritis, and malignancy. Firstly, principal component analysis was carried out for the pretreated sample spectra. Principal components analysis is a quantitatively rigorous method for achieving the simplification. The method generates a new set of variables, called principal components. Each principal component is a linear combination of the original variables. All the principal components are orthogonal to each other, so there is no redundant information. The principal components as a whole form an orthogonal basis for the space of the data. And then, the scores of principal components were selected as input to train the PNN model. Finally, PNN model was established. In this experiment, a total of 118 gastric endoscopic biopsies, including 35 cases of cancer, 64 cases of gastritis, and 19 healthy tissue samples, were obtained at the First Hospital of Xi’an Jiaotong University, China. Fifty nine samples were selected to establish the PNN classification model. The rest of the samples were used as the test set to valid the discriminant analysis model. The total discrimination correctness of normal, inflammation and gastric cancer achieved 81.4%.