(untitled) (ENTRENOVA - ENTerprise REsearch InNOVAtion Journal, 2025)
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.