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

Abstract

Breast cancer has the highest incidence and mortality in women worldwide. Early and accurate detection of the disease is crucial for reducing mortality rates. Tumours 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 tumours. However, various pathological processes can produce significant and unpredictable changes in body temperature. These limitations suggest thermal imaging should be used as an adjuvant examination, not a diagnostic test. Another limitation is the low sensitivity to tiny and deep tumours, often found in the analysis of surface temperatures using thermal images. Even the use of artificial intelligence directly on these images has failed to accurately locate and detect the tumour 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 tumours is circumvented by utilizing the concept of thermal impedance and artificial intelligence techniques. 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, deep learning libraries, and network training using convolutional neural networks. Lastly, we present tumour location and size estimates based on thermographic images obtained from simulated thermal models of a breast.