In vitro analysis of breast tumour detection using rotational infrared thermal imaging and machine learning techniques

Breast cancer is the most common cancer affecting women worldwide, and various methods, such as biopsy, mammography, 3D tomosynthesis, MRI, ultrasonography, and infrared thermal imaging (ITI), are utilized for its early detection. ITI is a technique that detects variations in thermal patterns on the breast surface, which are caused by the higher metabolic activity and vascularisation of cancerous cells. I As a radiation-free, non-invasive, and cost-effective screening method, ITI has been studied using in-silico, in-vivo, and in-vitro approaches to enhance its diagnostic performance and develop reliable imaging algorithms. Conventional ITI in in-vivo studies is limited by fixed imaging positions, making it difficult to detect deep or hidden tumours. To address these limitations, this study introduces a rotational ITI method integrated with machine learning algorithms in an in-vitro environment. The proposed method generates datasets with varying tumour depths for comprehensive algorithmic analysis. It captures thermal images from four distinct positions (0°, 90°, 180°, and 270°), enabling a more thorough evaluation of the phantom breast surface. Using the combined dataset, which incorporates information from all four positions, the Convolutional Autoencoder and Support Vector Machines methods achieved an accuracy of 98.28%, sensitivity of 97.75%, specificity of 98.82%, and an F1 score of 98.29%.