  To detect infected wounds in Diabetic Foot Ulcers (DFUs) from photographs,
preventing severe complications and amputations. Methods: This paper proposes
the Guided Conditional Diffusion Classifier (ConDiff), a novel deep-learning
infection detection model that combines guided image synthesis with a denoising
diffusion model and distance-based classification. The process involves (1)
generating guided conditional synthetic images by injecting Gaussian noise to a
guide image, followed by denoising the noise-perturbed image through a reverse
diffusion process, conditioned on infection status and (2) classifying
infections based on the minimum Euclidean distance between synthesized images
and the original guide image in embedding space. Results: ConDiff demonstrated
superior performance with an accuracy of 83% and an F1-score of 0.858,
outperforming state-of-the-art models by at least 3%. The use of a triplet loss
function reduces overfitting in the distance-based classifier. Conclusions:
ConDiff not only enhances diagnostic accuracy for DFU infections but also
pioneers the use of generative discriminative models for detailed medical image
analysis, offering a promising approach for improving patient outcomes.
