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Conference Paper
Advancing Diabetic Foot Ulcer Diagnosis with Self-Supervised Feature Learning
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- Authors
- Jiwon Park, Seihyoung Lee, Yun Ji Ban, Jeong Eun Kim
- Issue Date
- 2024-12
- Citation
- International Conference on Bioinformatics and Biomedicine (BIBM) 2024, pp.7105-7107
- Language
- English
- Type
- Conference Paper
- Abstract
- 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.
- KSP Keywords
- Baseline model, Diabetic foot ulcer, Effective treatment, F1-score, Feature extractioN, Fine-tuning, Limited labeled data, Skin diseases, Supervised Feature learning, self-supervised learning