| Texto completo | |
| Autor(es): |
Oliveira, Guilherme C.
;
Ngo, Quoc C.
;
Papa, Joao P.
;
Kumar, Dinesh
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2024 IEEE 37TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, CBMS 2024; v. N/A, p. 6-pg., 2024-01-01. |
| Resumo | |
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) | |
| Processo FAPESP: | 19/07665-4 - Centro de Inteligência Artificial |
| Beneficiário: | Fabio Gagliardi Cozman |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa Aplicada |
| Processo FAPESP: | 23/14197-2 - HIDE - Detecção de de Hipomímia Utilizando Arquiteturas Leves |
| Beneficiário: | João Paulo Papa |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria |
| Beneficiário: | Francisco Louzada Neto |
| Modalidade de apoio: | Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs |