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Chronic Wound Image Augmentation and Assessment Using Semi-Supervised Progressive Multi-Granularity EfficientNet
Ziyang Liu1, Emmanuel Agu1, Peder Pedersen2
1Computer Science DepartmentWorcester Polytechnic Institute Worcester MA 01609 USA.
IEEE Open Journal of Engineering in Medicine and Biology
|June 20, 2024
Summary
Semi-supervised learning effectively augmented a small wound dataset using unlabeled images, significantly improving deep learning-based wound assessment accuracy. This method enhances chronic wound grading performance.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Vision
Background:
- Chronic wound assessment relies on standardized tools like the Photographic Wound Assessment Tool (PWAT).
- Deep learning models require large, well-annotated datasets, which are often scarce for specialized medical data like wound images.
- Data augmentation techniques are crucial for improving model performance with limited datasets.
Purpose of the Study:
- To augment a small, imbalanced wound image dataset using semi-supervised learning with a larger unlabeled dataset.
- To develop and evaluate a deep learning model for comprehensive wound assessment based on the augmented dataset.
- To compare the performance of the proposed method against baseline models and prior state-of-the-art approaches.
Main Methods:
- Utilized the Photographic Wound Assessment Tool (PWAT) to define 8 key wound attributes for assessment.
- Employed semi-supervised learning and a Progressive Multi-Granularity (PMG) training mechanism to leverage 9870 unlabeled wound images alongside a 1639 labeled image dataset.
- Applied the EfficientNet Convolutional Neural Network for wound scoring on the augmented dataset.
Main Results:
- The Semi-Supervised PMG EfficientNet (SS-PMG-EfficientNet) achieved average classification accuracies and F1 scores of approximately 90% for all 8 PWAT sub-scores.
- The proposed approach outperformed existing baseline models and showed a 7% improvement over the prior state-of-the-art.
- Generative Adversarial Networks (GANs) for synthetic wound image generation did not enhance wound assessment performance.
Conclusions:
- Semi-supervised learning is a powerful technique for leveraging unlabeled medical data to improve deep learning model performance.
- The SS-PMG-EfficientNet approach demonstrates significant potential for accurate and automated deep learning-based wound grading.
- This study highlights the effectiveness of semi-supervised learning in overcoming data limitations for specialized medical image analysis.
