Feature fusion combined with tissue Raman spectroscopy to screen cervical cancer,Journal of Raman Spectroscopy

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Feature fusion combined with tissue Raman spectroscopy to screen cervical cancer
Journal of Raman Spectroscopy ( IF 2.4 ) Pub Date : 2021-08-31 , DOI: 10.1002/jrs.6246
Bo Yang ₁ , Cheng Chen ₂ , Fangfang Chen ₁ , Cailing Ma ₃ , Chen Chen ₁ , Huiting Zhang ₁ , Rui Gao ₁ , Shuailei Zhang ₁ , Xiaoyi Lv _{2,

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Affiliation

In this experiment, we collected 45 samples of cervicitis, 29 samples of low-grade squamous intraepithelial lesion (LSIL), 44 samples of high-grade squamous intraepithelial lesion (HSIL), 39 samples of cervical squamous cell carcinoma, and 38 cases of cervical adenocarcinoma. After preprocessing of the Raman spectral data, partial least squares (PLS) was used to reduce the dimensionality, and then extreme gradient boosting (XGBoost) was used for feature selection to obtain the first 30-dimensional features. The preprocessed Raman spectral data also used a fast Fourier transform (FFT) to obtain amplitude information, and then PLS and XGBoost were used to obtain the first 30-dimensional features. Finally, K nearest neighbor (KNN), extreme learning machine (ELM), artificial bee colony support vector machine (ABC-SVM), support vector machine optimized by the cuckoo search algorithm (CS-SVM), particle swarm optimization coupled with support vector machine (PSO-SVM), and the convolutional neural network combined with long- and short-term memory (CNN-LSTM) classification models were established. In the raw Raman spectral features experiments, the classification accuracies of KNN, ELM, ABC-SVM, CS-SVM, PSO-SVM, and CNN-LTSM were 60.76%, 65.81%, 76.21%, 77.66%, 73.50%, and 69.19%, respectively. In the feature fusion experiments, the classification accuracies were 60.91%, 67.84%, 77.64%, 78.49%, 75.54%, and 70.72%, respectively. The experimental results show that feature fusion can further improve model performance regardless of whether using linear classification models or nonlinear classification models. Therefore, it provides a new strategy for extracting features and screening multiple cervical pathological tissues in the future.

中文翻译：

特征融合结合组织拉曼光谱筛查宫颈癌

本实验共采集宫颈炎45例、低级别鳞状上皮内病变（LSIL）29例、高级别鳞状上皮内病变（HSIL）44例、宫颈鳞癌39例、宫颈鳞癌38例。腺癌。对拉曼光谱数据进行预处理后，使用偏最小二乘法（PLS）降维，然后使用极限梯度提升（XGBoost）进行特征选择，得到前30维特征。预处理后的拉曼光谱数据也使用快速傅立叶变换（FFT）获得幅度信息，然后使用PLS和XGBoost获得前30维特征。最后，K最近邻（KNN）、极限学习机（ELM）、人工蜂群支持向量机（ABC-SVM）、通过布谷鸟搜索算法优化的支持向量机 (CS-SVM)、粒子群优化结合支持向量机 (PSO-SVM) 以及结合长短期记忆的卷积神经网络 (CNN-LSTM) 分类模型成立。在原始拉曼光谱特征实验中，KNN、ELM、ABC-SVM、CS-SVM、PSO-SVM和CNN-LTSM的分类精度分别为60.76%、65.81%、76.21%、77.66%、73.50%和69.19 ％，分别。在特征融合实验中，分类准确率分别为60.91%、67.84%、77.64%、78.49%、75.54%和70.72%。实验结果表明，无论使用线性分类模型还是非线性分类模型，特征融合都能进一步提高模型性能。所以，

更新日期：2021-11-09

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