Improving the performance of the echinococcosis diagnosis model based on serum Raman spectroscopy via the integration of convolutional neural network and support vector machine

IF 4.6 2区 化学 Q1 SPECTROSCOPY
Yukang Huang , Jiahui Huang , Xiangxiang Zheng , Aian Wu , Guohua Wu , Liang Xu , Guodong Lü
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引用次数: 0

Abstract

Echinococcosis is a zoonotic parasitic disease characterized by its insidious nature and severe health impacts. Rapid and accurate screening is crucial for subsequent treatment. Previous studies have demonstrated that Raman spectroscopy combined with machine learning or deep learning can be used for rapid diagnosis of echinococcosis, but there remains room for improving diagnostic accuracy. Therefore, this study proposes combining a convolutional neural network with a support vector machine (CNN-SVM) for the analysis of serum Raman spectra, aiming to achieve high-accuracy classification of echinococcosis, liver cirrhosis, hepatocellular carcinoma, and normal control groups. After collecting the spectra of 573 serum samples, spectral features were extracted by the CNN and then classified using the SVM. The results show that the classification accuracy of CNN-SVM model is 96.5 %, which is better than the CNN (92.3 %) and SVM (89.3 %) used alone. Furthermore, in the binary classification task of detecting echinococcosis versus non-echinococcosis cases, the CNN-SVM model also achieved an accuracy of 96.5 %, surpassing the traditional dot immunogold filtration assay (88.7 %). In conclusion, the proposed CNN-SVM model demonstrates superior diagnostic performance for echinococcosis and holds significant clinical application potential.

Abstract Image

基于卷积神经网络和支持向量机的融合改进了基于血清拉曼光谱的棘球蚴病诊断模型的性能。
棘球蚴病是一种人畜共患的寄生虫病,具有隐匿性和严重的健康影响。快速准确的筛查对后续治疗至关重要。先前的研究表明,拉曼光谱结合机器学习或深度学习可用于棘球蚴病的快速诊断,但诊断准确性仍有提高的空间。因此,本研究提出将卷积神经网络与支持向量机(CNN-SVM)相结合用于血清拉曼光谱分析,旨在实现棘球蚴病、肝硬化、肝细胞癌和正常对照组的高精度分类。在采集573份血清样本的光谱后,通过CNN提取光谱特征,然后使用SVM进行分类。结果表明,CNN-SVM模型的分类准确率为96.5%,优于单独使用CNN(92.3%)和SVM(89.3%)。此外,在检测棘球蚴病与非棘球蚴病的二元分类任务中,CNN-SVM模型的准确率也达到了96.5%,超过了传统的点免疫金过滤法(88.7%)。综上所述,本文提出的CNN-SVM模型对棘球蚴病具有较好的诊断效果,具有较大的临床应用潜力。
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来源期刊
CiteScore
8.40
自引率
11.40%
发文量
1364
审稿时长
40 days
期刊介绍: Spectrochimica Acta, Part A: Molecular and Biomolecular Spectroscopy (SAA) is an interdisciplinary journal which spans from basic to applied aspects of optical spectroscopy in chemistry, medicine, biology, and materials science. The journal publishes original scientific papers that feature high-quality spectroscopic data and analysis. From the broad range of optical spectroscopies, the emphasis is on electronic, vibrational or rotational spectra of molecules, rather than on spectroscopy based on magnetic moments. Criteria for publication in SAA are novelty, uniqueness, and outstanding quality. Routine applications of spectroscopic techniques and computational methods are not appropriate. Topics of particular interest of Spectrochimica Acta Part A include, but are not limited to: Spectroscopy and dynamics of bioanalytical, biomedical, environmental, and atmospheric sciences, Novel experimental techniques or instrumentation for molecular spectroscopy, Novel theoretical and computational methods, Novel applications in photochemistry and photobiology, Novel interpretational approaches as well as advances in data analysis based on electronic or vibrational spectroscopy.
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