  Diabetic Foot Ulcer (DFU) is a serious skin wound requiring specialized care.
However, real DFU datasets are limited, hindering clinical training and
research activities. In recent years, generative adversarial networks and
diffusion models have emerged as powerful tools for generating synthetic images
with remarkable realism and diversity in many applications. This paper explores
the potential of diffusion models for synthesizing DFU images and evaluates
their authenticity through expert clinician assessments. Additionally,
evaluation metrics such as Frechet Inception Distance (FID) and Kernel
Inception Distance (KID) are examined to assess the quality of the synthetic
DFU images. A dataset of 2,000 DFU images is used for training the diffusion
model, and the synthetic images are generated by applying diffusion processes.
The results indicate that the diffusion model successfully synthesizes visually
indistinguishable DFU images. 70% of the time, clinicians marked synthetic DFU
images as real DFUs. However, clinicians demonstrate higher unanimous
confidence in rating real images than synthetic ones. The study also reveals
that FID and KID metrics do not significantly align with clinicians'
assessments, suggesting alternative evaluation approaches are needed. The
findings highlight the potential of diffusion models for generating synthetic
DFU images and their impact on medical training programs and research in wound
detection and classification.
