[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119443-en":3,"doc-seo-119443-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119443,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Breast Cancer Detection from Thermal Images using Machine Learning","The study presents an advanced machine-learning strategy for breast cancer detection using thermal images, emphasizing critical features that encode geometric and structural information. A patient thermal-image dataset supports feature extraction, followed by integration into a decision tree model to achieve 92% classification accuracy. The work positions this feature-driven learning pipeline as a way to raise diagnostic precision and consistency compared with traditional imaging workflows. Results suggest the approach can strengthen early and accurate breast cancer screening by complementing conventional methods.","Pechkova, Sijche; Venger, Lyudmyla; Andonovski, Dragana; Andonovic, Beti  \nArticle  \nBreast Cancer Detection from Thermal Images using Machine Learning  \nENTRENOVA-ENTerprise REsearch InNOVAtion  \nProvided in Cooperation with:  \nIRENET-Society for Advancing Innovation and Research in Economy, Zagreb  \nSuggested Citation: Pechkova, Sijche; Venger, Lyudmyla; Andonovski, Dragana; Andonovic, Beti (2025) : Breast Cancer Detection from Thermal Images using Machine Learning, ENTRENOVAENTerprise REsearch InNOVAtion, ISSN 2706-4735, IRENET-Society for Advancing Innovation and Research in Economy, Zagreb, Vol. 10, Iss. 1, pp. 567-577, [https://doi.org/10.54820/entrenova-2024-0042](https://doi.org/10.54820/entrenova-2024-0042)  \nThis Version is available at:  \n[https://hdl.handle.net/10419/317985](https://hdl.handle.net/10419/317985)  \nStandard-Nutzungsbedingungen:  \nDie Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.  \nSie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.  \nSofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.  \nTerms of use:  \nDocuments in EconStor maybe saved and copied foryour personal and scholarly purposes.  \nYou are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public.  \nIf the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence.  \n[https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)  \nBreast Cancer Detection from Thermal Images using Machine Learning  \nSijche Pechkova  \nFaculty of Technology and Metallurgy, Skopje, North Macedonia Lyudmyla Venger  \nIPectus Project, Berlin, Germany Dragana Andonovski  \nNorth Kansas City Hospital, Missouri, United States Beti Andonovic  \nFaculty of Technology and Metallurgy, Skopje, North Macedonia  \nAbstract  \nIn this study, the authors propose an advanced strategy to analyze thermal images for breast cancer detection employing machine learning techniques. By focusing on critical features that capture geometric and structural information in thermal images, the aim is to elevate the precision and uniformity of breast cancer diagnostics. The dataset comprises thermal images from patients with breast cancer; these vital features are extracted and integrated into proposed decision tree model, resulting ina classification accuracy of 92% . This highlights the utility of combining specialized features with machine learning algorithms in medical image analysis. Consequently, the findings suggest that this approach can substantially enhance traditional imaging methods, establishing a robust basis for early and accurate breast cancer detection.  \nKeywords: breast cancer, thermal images, machine learning  \nJEL classification: Y80  \nPaper type: Research article  \nReceived: 12 February 2024  \nAccepted: 29 Jun 2024  \nDOI: 10.54820/entrenova-2024-0042  \nIntroduction  \nThere are many studies that support thermography as a promising supplementary tool rather than a standalone solution for breast cancer screening. They suggest it may benefit patients at high risk or with specific breast characteristics, but that standardization and further research are needed to optimize its role. The consensus is that thermography’s non-invasive nature and potential to detect heat irregularities make it a valuable addition to mammography, especially with advances in AI and machine learning improving its diagnostic capacity. ","cbCaijehX3zT3pif","https://ap.wps.com/l/cbCaijehX3zT3pif","pdf",619833,1,12,"English","en",105,"# Introduction\n## Thermography as a supplementary screening tool\n## Evidence from prior diagnostic studies\n## Feature extraction and infrared imaging techniques\n## Segmentation methods for thermographic breast images","[{\"question\":\"What is the main approach proposed for breast cancer detection in the study?\",\"answer\":\"The authors propose a machine-learning strategy that analyzes thermal images by extracting critical geometric and structural features and using them in a decision tree model.\"},{\"question\":\"How is the model performance reported?\",\"answer\":\"The extracted features integrated into the decision tree model achieve a classification accuracy of 92%.\"},{\"question\":\"Why is thermography considered useful alongside mammography?\",\"answer\":\"Thermography is non-invasive and radiation-free and can detect heat irregularities; however, prior studies note that sensitivity and specificity can be inconsistent, so it is typically used as an adjunct rather than a standalone replacement.\"}]","Breast Cancer Detection from Thermal Images using Machine Learning | PDF",1785724312,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"breast-cancer-detection-from-thermal-images-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/breast-cancer-detection-from-thermal-images-using-machine-learning/119443/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main approach proposed for breast cancer detection in the study?","Question",{"text":75,"@type":76},"The authors propose a machine-learning strategy that analyzes thermal images by extracting critical geometric and structural features and using them in a decision tree model.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the model performance reported?",{"text":80,"@type":76},"The extracted features integrated into the decision tree model achieve a classification accuracy of 92%.",{"name":82,"@type":73,"acceptedAnswer":83},"Why is thermography considered useful alongside mammography?",{"text":84,"@type":76},"Thermography is non-invasive and radiation-free and can detect heat irregularities; 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