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

Author(s)

Oliveira, Guilherme C. ; Ngo, Quoc C. ; Papa, Joao P. ; Kumar, Dinesh

• Document type: Journal article
• Source: 2024 IEEE 37TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, CBMS 2024; v. N/A, p. 6-pg., 2024-01-01.

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. (AU)

• FAPESP's process: 19/07665-4 - Center for Artificial Intelligence
• Grantee: Fabio Gagliardi Cozman
• Support Opportunities: Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program
• FAPESP's process: 23/14197-2 - HIDE - HypomImia DetEction using lightweight architectures
• Grantee: João Paulo Papa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC