Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers

• URL: http://arxiv.org/abs/2405.00858v1
• Date: Wed, 1 May 2024 20:47:06 GMT
• Title: Guided Conditional Diffusion Classifier (ConDiff) for Enhanced Prediction of Infection in Diabetic Foot Ulcers
• Authors: Palawat Busaranuvong, Emmanuel Agu, Deepak Kumar, Shefalika Gautam, Reza Saadati Fard, Bengisu Tulu, Diane Strong,
• Abstract summary: ConDiff is a novel deep-learning infection detection model that combines guided image synthesis with a Conditional denoising diffusion model and distance-based classification. 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%.
• Score: 2.4548085068515286
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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