School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Photodiagnosis Photodyn Ther. 2023 Jun;42:103340. doi: 10.1016/j.pdpdt.2023.103340. Epub 2023 Feb 27.
In this study, a minimally invasive test method for cervical cancer in vitro was proposed by comparing Raman spectroscopy with support vector machine (SVM) model and deep belief network (DBN) model. The serum Raman spectra of cervical cancer, hysteromyoma, and healthy people were collected. After data processing, SVM classification model and DBN classification model were built respectively. The experimental results show that when the DBN network algorithm is used, the sample test set can be divided accurately and the result of cross-validation is ideal. Compared with the traditional SVM algorithm, this method firstly screened the effective feature matrix from the data, and then classified the data. With high efficiency and accuracy, based on 445 samples collected, this method improved the accuracy by 13.93%±2.47% compared with the SVM method, and provided a new direction and idea for the in vitro diagnosis of cervical diseases.
本研究通过比较拉曼光谱与支持向量机（SVM）模型和深度置信网络（DBN）模型，提出了一种体外宫颈癌微创检测方法。采集宫颈癌、子宫肌瘤和健康人群的血清拉曼光谱。经过数据处理，分别建立了 SVM 分类模型和 DBN 分类模型。实验结果表明，当使用 DBN 网络算法时，可以准确地对样本测试集进行分类，并且交叉验证的结果较为理想。与传统的 SVM 算法相比，该方法首先从数据中筛选出有效的特征矩阵，然后对数据进行分类。基于采集到的 445 个样本，该方法的准确率提高了 13.93%±2.47%，为宫颈疾病的体外诊断提供了新的方向和思路。
