Improving the performance of the echinococcosis diagnosis model based on serum Raman spectroscopy via the integration of convolutional neural network and support vector machine
{"title":"Improving the performance of the echinococcosis diagnosis model based on serum Raman spectroscopy via the integration of convolutional neural network and support vector machine","authors":"Yukang Huang , Jiahui Huang , Xiangxiang Zheng , Aian Wu , Guohua Wu , Liang Xu , Guodong Lü","doi":"10.1016/j.saa.2025.126945","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":433,"journal":{"name":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","volume":"346 ","pages":"Article 126945"},"PeriodicalIF":4.6000,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1386142525012521","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SPECTROSCOPY","Score":null,"Total":0}
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.
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.