Rapid diagnosis of cervical cancer based on serum FTIR spectroscopy and support vector machines
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 betw...
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| Veröffentlicht in: | Lasers in medical science 2023-11, Vol.38 (1), p.276-276, Article 276 |
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| description | 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. |
| doi_str_mv | 10.1007/s10103-023-03930-y |
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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.</description><identifier>ISSN: 1435-604X</identifier><identifier>ISSN: 0268-8921</identifier><identifier>EISSN: 1435-604X</identifier><identifier>DOI: 10.1007/s10103-023-03930-y</identifier><identifier>PMID: 38001244</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Accuracy ; Algorithms ; Artificial neural networks ; Cancer ; Cervical cancer ; Datasets ; Deep learning ; Dentistry ; Diagnosis ; Female ; Fourier transforms ; Gynecological diseases ; Humans ; Infrared spectroscopy ; Kernel functions ; Lasers ; Learning algorithms ; Machine learning ; Medicine ; Medicine & Public Health ; Neural networks ; Neural Networks, Computer ; Optical Devices ; Optics ; Original Article ; Photonics ; Quantum Optics ; Recognition ; Spectroscopy ; Spectroscopy, Fourier Transform Infrared - methods ; Spectrum analysis ; Support Vector Machine ; Support vector machines ; Test sets ; Tumors ; Uterine Cervical Neoplasms - diagnosis</subject><ispartof>Lasers in medical science, 2023-11, Vol.38 (1), p.276-276, Article 276</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2023. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c326t-521194c441761991013e38fb778b7dc8d3847572f12d586b61e75a94a4fc2c9f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10103-023-03930-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10103-023-03930-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38001244$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Xue, Yunfei</creatorcontrib><creatorcontrib>Zheng, Xiangxiang</creatorcontrib><creatorcontrib>Wu, Guohua</creatorcontrib><creatorcontrib>Wang, Jing</creatorcontrib><title>Rapid diagnosis of cervical cancer based on serum FTIR spectroscopy and support vector machines</title><title>Lasers in medical science</title><addtitle>Lasers Med Sci</addtitle><addtitle>Lasers Med Sci</addtitle><description>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.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Cancer</subject><subject>Cervical cancer</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Dentistry</subject><subject>Diagnosis</subject><subject>Female</subject><subject>Fourier transforms</subject><subject>Gynecological diseases</subject><subject>Humans</subject><subject>Infrared spectroscopy</subject><subject>Kernel functions</subject><subject>Lasers</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Optical Devices</subject><subject>Optics</subject><subject>Original Article</subject><subject>Photonics</subject><subject>Quantum Optics</subject><subject>Recognition</subject><subject>Spectroscopy</subject><subject>Spectroscopy, Fourier Transform Infrared - methods</subject><subject>Spectrum analysis</subject><subject>Support Vector Machine</subject><subject>Support vector machines</subject><subject>Test sets</subject><subject>Tumors</subject><subject>Uterine Cervical Neoplasms - diagnosis</subject><issn>1435-604X</issn><issn>0268-8921</issn><issn>1435-604X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kF1LwzAUhoMobk7_gBcS8Mabar7atJciTgcDYUzwLqRpOjvWpuasg_57sw8_8MKLkJfkOe8550XokpJbSoi8A0oo4RFh4fCMk6g_QkMqeBwlRLwd_9IDdAawJITKhPJTNOBp0EyIIVIz3VYFLiq9aBxUgF2JjfWbyugVNroJGucabIFdg8H6rsbj-WSGobVm7R0Y1_ZYNwWGrm2dX-NNeHce19q8V42Fc3RS6hXYi8M9Qq_jx_nDczR9eZo83E8jw1myjmJGaSaMENsJsywsxi1Py1zKNJeFSQueChlLVlJWxGmSJ9TKWGdCi9Iwk5V8hG72vq13H52FtaorMHa10o11HSiWZsEiJiwJ6PUfdOk634TpdhTjUmRpoNieMmFL8LZUra9q7XtFidrGr_bxqxC_2sWv-lB0dbDu8toW3yVfeQeA7wEIX83C-p_e_9h-Al1Yj38</recordid><startdate>20231125</startdate><enddate>20231125</enddate><creator>Xue, Yunfei</creator><creator>Zheng, Xiangxiang</creator><creator>Wu, Guohua</creator><creator>Wang, Jing</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7SP</scope><scope>7U5</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>L7M</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope></search><sort><creationdate>20231125</creationdate><title>Rapid diagnosis of cervical cancer based on serum FTIR spectroscopy and support vector machines</title><author>Xue, Yunfei ; Zheng, Xiangxiang ; Wu, Guohua ; Wang, Jing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c326t-521194c441761991013e38fb778b7dc8d3847572f12d586b61e75a94a4fc2c9f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Cancer</topic><topic>Cervical cancer</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Dentistry</topic><topic>Diagnosis</topic><topic>Female</topic><topic>Fourier transforms</topic><topic>Gynecological diseases</topic><topic>Humans</topic><topic>Infrared spectroscopy</topic><topic>Kernel functions</topic><topic>Lasers</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neural networks</topic><topic>Neural Networks, Computer</topic><topic>Optical Devices</topic><topic>Optics</topic><topic>Original Article</topic><topic>Photonics</topic><topic>Quantum Optics</topic><topic>Recognition</topic><topic>Spectroscopy</topic><topic>Spectroscopy, Fourier Transform Infrared - methods</topic><topic>Spectrum analysis</topic><topic>Support Vector Machine</topic><topic>Support vector machines</topic><topic>Test sets</topic><topic>Tumors</topic><topic>Uterine Cervical Neoplasms - diagnosis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xue, Yunfei</creatorcontrib><creatorcontrib>Zheng, Xiangxiang</creatorcontrib><creatorcontrib>Wu, Guohua</creatorcontrib><creatorcontrib>Wang, Jing</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Proquest Nursing & Allied Health Source</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><jtitle>Lasers in medical science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xue, Yunfei</au><au>Zheng, Xiangxiang</au><au>Wu, Guohua</au><au>Wang, Jing</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Rapid diagnosis of cervical cancer based on serum FTIR spectroscopy and support vector machines</atitle><jtitle>Lasers in medical science</jtitle><stitle>Lasers Med Sci</stitle><addtitle>Lasers Med Sci</addtitle><date>2023-11-25</date><risdate>2023</risdate><volume>38</volume><issue>1</issue><spage>276</spage><epage>276</epage><pages>276-276</pages><artnum>276</artnum><issn>1435-604X</issn><issn>0268-8921</issn><eissn>1435-604X</eissn><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.</abstract><cop>London</cop><pub>Springer London</pub><pmid>38001244</pmid><doi>10.1007/s10103-023-03930-y</doi><tpages>1</tpages></addata></record> |
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| subjects | Accuracy Algorithms Artificial neural networks Cancer Cervical cancer Datasets Deep learning Dentistry Diagnosis Female Fourier transforms Gynecological diseases Humans Infrared spectroscopy Kernel functions Lasers Learning algorithms Machine learning Medicine Medicine & Public Health Neural networks Neural Networks, Computer Optical Devices Optics Original Article Photonics Quantum Optics Recognition Spectroscopy Spectroscopy, Fourier Transform Infrared - methods Spectrum analysis Support Vector Machine Support vector machines Test sets Tumors Uterine Cervical Neoplasms - diagnosis |
| title | Rapid diagnosis of cervical cancer based on serum FTIR spectroscopy and support vector machines |
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