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Sensors 2017, 17(3), 627; doi:10.3390/s17030627

Diagnosis of Breast Cancer Tissues Using 785 nm Miniature Raman Spectrometer and Pattern Regression

1,* , 1,* and 2
1
School of Instrumentation Science and Opto-Electronics Engineering, Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing 100191, China
2
Department of General Surgery, Third Hospital, Peking University, Beijing 100083, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Sebastian Wachsmann-Hogiu and Zachary J. Smith
Received: 25 January 2017 / Revised: 13 March 2017 / Accepted: 16 March 2017 / Published: 19 March 2017
(This article belongs to the Special Issue Applications of Raman Spectroscopy in Biosensors)
View Full-Text   |   Download PDF [762 KB, uploaded 19 March 2017]   |  

Abstract

For achieving the development of a portable, low-cost and in vivo cancer diagnosis instrument, a laser 785 nm miniature Raman spectrometer was used to acquire the Raman spectra for breast cancer detection in this paper. However, because of the low spectral signal-to-noise ratio, it is difficult to achieve high discrimination accuracy by using the miniature Raman spectrometer. Therefore, a pattern recognition method of the adaptive net analyte signal (NAS) weight k-local hyperplane (ANWKH) is proposed to increase the classification accuracy. ANWKH is an extension and improvement of K-local hyperplane distance nearest-neighbor (HKNN), and combines the advantages of the adaptive weight k-local hyperplane (AWKH) and the net analyte signal (NAS). In this algorithm, NAS was first used to eliminate the influence caused by other non-target factors. Then, the distance between the test set samples and hyperplane was calculated with consideration of the feature weights. The HKNN only works well for small values of the nearest-neighbor. However, the accuracy decreases with increasing values of the nearest-neighbor. The method presented in this paper can resolve the basic shortcoming by using the feature weights. The original spectra are projected into the vertical subspace without the objective factors. NAS was employed to obtain the spectra without irrelevant information. NAS can improve the classification accuracy, sensitivity, and specificity of breast cancer early diagnosis. Experimental results of Raman spectra detection in vitro of breast tissues showed that the proposed algorithm can obtain high classification accuracy, sensitivity, and specificity. This paper demonstrates that the ANWKH algorithm is feasible for early clinical diagnosis of breast cancer in the future. View Full-Text
Keywords: Raman spectrometer; breast cancer; pattern recognition; adaptive net analyte signal weight K-local hyperplane (ANWKH) Raman spectrometer; breast cancer; pattern recognition; adaptive net analyte signal weight K-local hyperplane (ANWKH)
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Li, Q.; Hao, C.; Xu, Z. Diagnosis of Breast Cancer Tissues Using 785 nm Miniature Raman Spectrometer and Pattern Regression. Sensors 2017, 17, 627.

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