[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-328905-105":59,"doc-detail-328905-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","employing-raman-spectroscopy-and-machine-learning-for-the-identification-of-breast-cancer","Employing Raman Spectroscopy and Machine Learning for the Identification of Breast Cancer","","Breast cancer presents a major health risk, and identifying cancerous mammary tissue during surgery is essential to remove tumors completely. This study integrates Raman spectroscopy with machine learning models—Random Forest, Support Vector Machine, and a Convolutional Neural Network—to distinguish normal from late-stage cancerous tissues in a murine setting. Reported classification accuracies reach 94.47% (RF), 96.76% (SVM), and 97.58% (CNN). The work also highlights spectral peaks linked to molecular differences, supporting a noninvasive, rapid intraoperative diagnostic tool.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/employing-raman-spectroscopy-and-machine-learning-for-the-identification-of-breast-cancer/328905/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/employing-raman-spectroscopy-and-machine-learning-for-the-identification-of-breast-cancer/328905.png","ImageObject",300,407,{"name":92,"@type":93},"Miles","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is intraoperative identification of breast cancer tissue important?","Question",{"text":112,"@type":113},"Accurate differentiation of cancerous from non-cancerous mammary tissue during surgery helps enable complete tumor removal and improves surgical decision-making.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which machine learning models were used with Raman spectroscopy?",{"text":117,"@type":113},"The study used Random Forest (RF), Support Vector Machine (SVM), and a Convolutional Neural Network (CNN) to classify tissue types from Raman spectra.",{"name":119,"@type":110,"acceptedAnswer":120},"What classification accuracies were reported for each model?",{"text":121,"@type":113},"RF achieved 94.47%, SVM achieved 96.76%, and CNN achieved 97.58% accuracy for distinguishing normal and late-stage cancerous tissue.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},328905,1790298449,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},13056703019404,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Zhang et al. Biological Procedures Online (2024) 26:28  \n[https://doi.org/10.1186/s12575-024-00255-0](https://doi.org/10.1186/s12575-024-00255-0)  \nBiological Procedures Online  \nRESEARCH Open Access  \nEmploying Raman Spectroscopy and Machine  Learning for the Identification of Breast  \nCancer  \nYa Zhang 1, Zheng Li 1, Zhongqiang Li 1, Huaizhi Wang 1, Dinkar Regmi 1, Jian Zhang2, Jiming Feng3, Shaomian Yao3 and Jian Xu 1*  \nAbstract  \nBackground Breast cancer poses a significant health risk to women worldwide, with approximately 30% being diagnosed annually in the United States. The identification of cancerous mammary tissues from non-cancerous ones during surgery is crucial for the complete removal of tumors.  \nResults Our study innovatively utilized machine learning techniques (Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)) alongside Raman spectroscopy to streamline and hasten the differentiation of normal and late-stage cancerous mammary tissues in mice. The classification accuracy rates achieved by these models were 94 .47% for RF, 96 . 76% for SVM, and 97 . 58% for CNN, respectively. To our best knowledge, this study was the first effort in comparing the effectiveness of these three machine-learning techniques in classifying breast cancer tissues based on their Raman spectra. Moreover, we innovatively identified specific spectral peaks that contribute to the molecular characteristics of the murine cancerous and non-cancerous tissues. Conclusions Consequently, our integrated approach of machine learning and Raman spectroscopy presents a noninvasive, swift diagnostic tool for breast cancer, offering promising applications in intraoperative settings.  \nKeywords Raman spectroscopy, Machine learning, Late-stage breast cancer; Cancerous and non-cancerous tissue classification  \n*Correspondence:  \nJian Xu  \n[jianxu1@lsu.edu](jianxu1@lsu.edu)  \n1Division of Electrical and Computer Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA 70803, USA  \n2Division of Computer Science & Engineering, College of Engineering, Louisiana State University, Baton Rouge, LA 70803, USA  \n3Department of Comparative Biomedical Science, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803, USA  \nBackground  \nBreast cancer is one of the most prevalent cancers diagnosed in females in the United States; new breast cancer accounts for 31% of estimated various cancers, and mortality of all types of breast cancer is the second out of all female cancerous diseases [1]. Regular breast cancer screening, including clinical breast exams and mammograms, has enhanced early detection rates and is crucial for the prognosis and treatment planning of surgery, radiation therapy, chemotherapy, and the latest targeted therapies [2]. The diagnosis of breast cancer is not only via imaging but also associated with histopathological analysis ofthe patient’s tissue, which is invasive and painful [3].  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/l](http://creativecommons.org/l)icenses/by/4.0/.  \nZhang et al. Biological Procedures Online (2024) 26:28  \nHaka and colleagues compare the","cbCaifvcf85yIzWz","https://ap.wps.com/l/cbCaifvcf85yIzWz","pdf",1421762,"English","# Abstract\n## Background\n## Results\n## Conclusions\n# Background\n## Existing screening and diagnostic approaches\n## Imaging modalities and limitations\n# Raman spectroscopy rationale","[{\"question\":\"Why is intraoperative identification of breast cancer tissue important?\",\"answer\":\"Accurate differentiation of cancerous from non-cancerous mammary tissue during surgery helps enable complete tumor removal and improves surgical decision-making.\"},{\"question\":\"Which machine learning models were used with Raman spectroscopy?\",\"answer\":\"The study used Random Forest (RF), Support Vector Machine (SVM), and a Convolutional Neural Network (CNN) to classify tissue types from Raman spectra.\"},{\"question\":\"What classification accuracies were reported for each model?\",\"answer\":\"RF achieved 94.47%, SVM achieved 96.76%, and CNN achieved 97.58% accuracy for distinguishing normal and late-stage cancerous tissue.\"}]","Employing Raman Spectroscopy and Machine Learning for the Identification of Breast Cancer | PDF",1789981590,23]