Exploration research on the fusion of multimodal spectrum technology to improve performance of rapid diagnosis scheme for thyroid dysfunction
| Autor: | Guodong Lv, Hongyi Li, Jun Tang, Guoli Du, Dongni Tong, Rumeng Si, Jiaqing Mo, Xiaoyi Lv, Hongbing Ma, Cheng Chen |
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| Rok vydání: | 2019 |
| Předmět: |
Technology
Support Vector Machine Computer science Feature extraction Thyroid Gland General Physics and Astronomy Spectrum Analysis Raman 01 natural sciences General Biochemistry Genetics and Molecular Biology Spectral line 010309 optics 0103 physical sciences General Materials Science Extreme learning machine Fusion Learning vector quantization Principal Component Analysis business.industry 010401 analytical chemistry General Engineering Pattern recognition General Chemistry 0104 chemical sciences Support vector machine Pattern recognition (psychology) Principal component analysis Artificial intelligence business Algorithms |
| Zdroj: | Journal of biophotonicsREFERENCES. 13(2) |
| ISSN: | 1864-0648 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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