Li Qingbo, Zhang Zhixiang, Ma Zhenhe
School of Instrumentation and Optoelectronic Engineering, Precision Opto-Mechatronics Technology Key Laboratory of Education Ministry, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing, 100191, China.
Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Detection Technology, Northeastern University, Qinhuangdao Campus, Qinhuangdao, 066004, China.
Heliyon. 2023 Jul 11;9(7):e18148. doi: 10.1016/j.heliyon.2023.e18148. eCollection 2023 Jul.
Raman spectroscopy, as a kind of molecular vibration spectroscopy, provides abundant information for measuring components and molecular structure in the early detection and diagnosis of breast cancer. Currently, portable Raman spectrometers have simplified and made equipment application more affordable, albeit at the cost of sacrificing the signal-to-noise ratio (SNR). Consequently, this necessitates a higher recognition rate from pattern recognition algorithms. Our study employs a feature fusion strategy to reduce the dimensionality of high-dimensional Raman spectra and enhance the discriminative information between normal tissues and tumors. In the conducted random experiment, the classifier achieved a performance of over 96% for all three average metrics: accuracy, sensitivity, and specificity. Additionally, we propose a multi-parameter serial encoding evolutionary algorithm (MSEA) and integrate it into the Adaptive Local Hyperplane K-nearest Neighbor classification algorithm (ALHK) for adaptive hyperparameter optimization. The implementation of serial encoding tackles the predicament of parallel optimization in multi-hyperparameter vector problems. To bolster the convergence of the optimization algorithm towards a global optimal solution, an exponential viability function is devised for nonlinear processing. Moreover, an improved elitist strategy is employed for individual selection, effectively eliminating the influence of probability factors on the robustness of the optimization algorithm. This study further optimizes the hyperparameter space through sensitivity analysis of hyperparameters and cross-validation experiments, leading to superior performance compared to the ALHK algorithm with manual hyperparameter configuration.
拉曼光谱作为一种分子振动光谱，在乳腺癌的早期检测和诊断中，为测量成分和分子结构提供了丰富信息。目前，便携式拉曼光谱仪简化了设备应用且降低了成本，尽管这是以牺牲信噪比（SNR）为代价的。因此，这就需要模式识别算法有更高的识别率。我们的研究采用特征融合策略来降低高维拉曼光谱的维度，并增强正常组织和肿瘤之间的判别信息。在进行的随机实验中，分类器在准确率、灵敏度和特异性这三个平均指标上的性能均超过了96%。此外，我们提出了一种多参数串行编码进化算法（MSEA），并将其集成到自适应局部超平面K近邻分类算法（ALHK）中进行自适应超参数优化。串行编码的实现解决了多超参数向量问题中并行优化的困境。为了增强优化算法向全局最优解的收敛性，设计了一个指数生存函数用于非线性处理。此外，采用了一种改进的精英策略进行个体选择，有效消除了概率因素对优化算法鲁棒性的影响。本研究通过超参数的灵敏度分析和交叉验证实验进一步优化了超参数空间，与手动配置超参数的ALHK算法相比，性能更优。
