| dc.description.abstract | Breast cancer is a leading cause of mortality
among women worldwide. Temperature-based techniques
have emerged as a promising approach for breast cancer
detection and prediction. This literature review aims to
comprehensively analyse the existing research on
mathematical models developed to predict the temperature
gradient between the surface and core of the female breast.
Various mathematical models, including Penne’s bioheat
transfer model, Wulff's model, Klinger's model, Chen and
Holmes' model, and the porous media model, have been
investigated. The strengths and limitations of each model,
as well as their application in breast cancer risk prediction,
have been examined. Additionally, the utilization of breast
models, sensors, and validation techniques has been
explored. The review highlights the need for further
research to address the limitations of existing models and
improve their accuracy in breast cancer diagnosis. The
findings provide valuable insights for advancing
temperature-based approaches and enhancing early
detection strategies. | en_US |
