Artificial Intelligence-Based Thermal Imaging for Breast Tumor Location and Size Estimation Using Thermal Impedance

Abstract

Tumors can be detected from a temperature gradient due to high vascularization and increased metabolic activity of cancer cells. Thermal infrared images have been recognized as potential alternatives to detect these tumors. However, even the use of artificial intelligence directly on these images has failed to accurately locate and detect the tumor size due to the low sensitivity of temperatures and position within the breast. Thus, we aimed to develop techniques based on applying the thermal impedance method and artificial intelligence to determine the origin of the heat source (abnormal cancer metabolism) and its size. The low sensitivity to tiny and deep tumors is circumvented by utilizing the concept of thermal impedance and artificial intelligence techniques such as deep learning. We describe the development of a thermal model and the creation of a database based on its solution. We also outline the choice of detectable parameters in the thermal image, the use of deep learning libraries, and network training using convolutional neural networks (CNNs). Lastly, we present tumor location and size estimates based on thermographic images obtained from simulated thermal models of a breast, using Cartesian geometry and a scanned geometric shape of an anatomical phantom model.