Advancing Diabetic Foot Ulcer Diagnosis with Self-Supervised Feature Learning

Diabetic foot ulcer (DFU) are a significant complication in diabetic patients, requiring accurate classification for effective treatment. This study investigates the use of self-supervised learning with Bootstrap Your Own Latent (BYOL) for feature extraction, combined with EfficientNetB0 as a downstream classifier. The proposed approach leverages unlabeled skin disease images for pretraining with BYOL, followed by fine-tuning on labeled data for four-class classification of infection, ischemia, both conditions, and other diabetic foot ulcers. We compared the performance of the BYOL pre-trained EfficientNetB0 model with a standard EfficientNetB0 classifier trained solely on labeled data. Our results demonstrate that the BYOL-based model achieved better performance in terms of accuracy, precision, recall, and f1-score. Additionally, Grad-CAM visualizations revealed that the BYOL-EfficientNetB0 model captures more accurate and relevant features compared to the baseline model. This study highlights the potential of self-supervised learning in improving the classification of DFU, especially in scenarios with limited labeled data.