A Stable Diffusion Approach for RGB to Thermal Image Conversion for Leg Ulcer Assessment

Abstract

Thermal imaging of venous leg ulcers has helped clinicians make informed wound management decisions. However, thermal cameras are not available in most clinics. To overcome this, we propose a pilot test using deep learning to estimate thermal images from RGB data of the ulcers. Our approach employs stable diffusion techniques, e.g., DreamBooth, LoRA, and ControlNet, to create thermal images from RGB data, addressing the limitations of cost and accessibility in conventional thermal imaging to assist clinicians in assessing the ulcers. While the images' visualization appears helpful, achieving an average structural similarity index measure (SSIM) score of 0.84, this study has yet to test their suitability for a computerized assessment of chronic wounds.