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.
Department of General Surgery, Third Hospital, Peking University, Beijing 100083, China.
Sensors (Basel). 2017 Mar 19;17(3):627. doi: 10.3390/s17030627.
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.
为了开发一种便携式、低成本的体内癌症诊断仪器，本文使用了一种 785nm 激光微型拉曼光谱仪来获取乳腺癌检测的拉曼光谱。然而，由于光谱信号噪声比较低，使用微型拉曼光谱仪很难实现高的鉴别精度。因此，提出了一种自适应分析物信号（NAS）权值 k-局部超平面（ANWKH）的模式识别方法来提高分类精度。ANWKH 是 K-局部超平面距离最近邻（HKNN）的扩展和改进，结合了自适应权值 k-局部超平面（AWKH）和分析物信号（NAS）的优点。在该算法中，首先使用 NAS 消除其他非目标因素的影响。然后，考虑特征权重计算测试集样本与超平面之间的距离。HKNN 仅适用于最近邻值较小的情况。然而，随着最近邻值的增加，准确性会降低。本文提出的方法可以通过使用特征权重来解决基本的缺点。原始光谱被投影到没有目标因素的垂直子空间中。NAS 用于获得没有无关信息的光谱。NAS 可以提高乳腺癌早期诊断的分类精度、灵敏度和特异性。体外乳腺组织拉曼光谱检测的实验结果表明，所提出的算法可以获得高的分类精度、灵敏度和特异性。本文表明，ANWKH 算法在未来可用于乳腺癌的早期临床诊断。
