Exploration research on the fusion of multimodal spectrum technology to improve performance of rapid diagnosis scheme for thyroid dysfunction. Issue 2 (19th November 2019) - Chen, Cheng; Du, Guoli; Tong, Dongni; Lv, Guodong; Lv, Xiaoyi; Si, Rumeng; Tang, Jun; Li, Hongyi; Ma, Hongbing; Mo, Jiaqing
Abstract: The spectral fusion by Raman spectroscopy and Fourier infrared spectroscopy combined with pattern recognition algorithms is utilized to diagnose thyroid dysfunction serum, and finds the spectral segment with the highest sensitivity to further advance diagnosis speed. Compared with the single infrared spectroscopy or Raman spectroscopy, the proposal can improve the detection accuracy, and can obtain more spectral features, indicating greater differences between thyroid dysfunction and normal serum samples. For discriminating different samples, principal component analysis (PCA) was first used for feature extraction to reduce the dimension of high‐dimension spectral data and spectral fusion. Then, support vector machine (SVM), back propagation neural network, extreme learning machine and learning vector quantization algorithms were employed to establish the discriminant diagnostic models. The accuracy of spectral fusion of the best analytical model PCA‐SVM, single Raman spectral accuracy and single infrared spectral accuracy is 83.48%, 78.26% and 80%, respectively. The accuracy of spectral fusion is higher than the accuracy of single spectrum in five classifiers. And the diagnostic accuracy of spectral fusion in the range of 2000 to 2500 cm −1 is 81.74%, which greatly improves the sample measure speed and data analysis speed than analysis of full spectra. The results from our study demonstrate that the serum spectral fusion technique combined with multivariate statistical methods have great potential for the screening of thyroid dysfunction. Abstract : Three hundred eighty‐two cases were divided into 267 cases as a training set and 115 cases as a test set. The Raman spectral data and the infrared spectral data of the training set and the test set were separately extracted by PCA, and matrixes of 267 × 7, 115 × 7, 267 × 4 and 115 × 4 were obtained. The two training set matrices were merged into a 267 × 11 matrix, and the two test set matrices were merged into a 115 × 11 matrix. Then, several pattern recognition algorithms were used for classification, and the results of the spectral fusion was compared with the results of the single‐spectrum analysis.