Izvorni znanstveni članak
https://doi.org/10.54820/entrenova-2024-0042
Breast Cancer Detection from Thermal Images using Machine Learning
Sijche Pechkova
; Faculty of Technology and Metallurgy, Skopje
Lyudmyla Venger
; IPectus Project, Berlin
Dragana Andonovski
; North Kansas City Hospital, Missouri
Beti Andonovic
; Faculty of Technology and Metallurgy, Skopje
Sažetak
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.
Ključne riječi
breast cancer,thermal images,machine learning
Hrčak ID:
322286
URI
Datum izdavanja:
13.11.2024.
Posjeta: 17 *