Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/317985 
Erscheinungsjahr: 
2025
Quellenangabe: 
[Journal:] ENTRENOVA - ENTerprise REsearch InNOVAtion [ISSN:] 2706-4735 [Volume:] 10 [Issue:] 1 [Year:] 2024 [Pages:] 567-577
Verlag: 
IRENET - Society for Advancing Innovation and Research in Economy, Zagreb
Zusammenfassung: 
In 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 in a 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.
Schlagwörter: 
breast cancer
thermal images
machine learning
JEL: 
Y80
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by-nc Logo
Dokumentart: 
Article

Datei(en):
Datei
Größe





Publikationen in EconStor sind urheberrechtlich geschützt.