Abstract：
Cervical cancer is one of the most common malignant tumors among female gynecological diseases. This paper aims to explore the feasibility of utilizing serum Fourier Transform Infrared (FTIR) spectroscopy, combined with machine learning and deep learning algorithms, to efficiently differentiate between healthy individuals, hysteromyoma patients, and cervical cancer patients. In this study, serum samples from 30 groups of hysteromyoma, 36 groups of cervical cancer, and 30 healthy groups were collected and FTIR spectra of each group were recorded. In addition, the raw datasets were averaged according to the number of scans to obtain an average dataset, and the raw datasets were spectrally enhanced to obtain an augmentation dataset, resulting in a total of three sets of data with sizes of 258, 96, and 1806, respectively. Then, the hyperparameters in the four kernel functions of the Support Vector Machine (SVM) model were optimized by grid search and leave-one-out (LOO) cross-validation. The resulting SVM models achieved recognition accuracies ranging from 85.0% to 100.0% on the test set. Furthermore, a one-dimensional convolutional neural network (1D-CNN) demonstrated a recognition accuracy of 75.0% to 90.0% on the test set. It can be concluded that the use of serum FTIR spectroscopy combined with the SVM algorithm for the diagnosis of cervical cancer has important medical significance.
摘要：
宫颈癌是女性妇科疾病中最常见的恶性肿瘤之一。本文旨在探索利用血清傅里叶变换红外(FTIR)光谱技术的可行性,结合机器学习和深度学习算法，为了有效区分健康的个体，子宫肌瘤患者，和宫颈癌患者。在这项研究中,来自30组子宫肌瘤的血清样本，36组宫颈癌，收集30个健康组,记录各组的FTIR光谱。此外,根据扫描次数对原始数据集进行平均，以获得平均数据集，并对原始数据集进行光谱增强以获得增强数据集，总共得到三组大小分别为258、96和1806的数据。然后，支持向量机（SVM）模型的四个核函数中的超参数通过网格搜索和留一法（LOO）交叉验证进行了优化。得到的SVM模型在测试集上实现了从85.0%到100.0%的识别精度。此外，一维卷积神经网络（1D-CNN）在测试集上显示出75.0%至90.0%的识别准确率。由此可以得出结论，血清FTIR光谱联合SVM算法的运用对宫颈癌的诊断具有主要的医学意义。
