Chronic wounds are a significant burden on individuals and the healthcare system, affecting millions of people and incurring high costs. Wound classification using deep learning techniques is a promising approach for faster diagnosis and treatment initiation. However, lack of high quality data to train the ML models is a major challenge to realize the potential of ML in wound care. In fact, data limitations are the biggest challenge in studies using medical or forensic imaging today. We study data augmentation techniques that can be used to overcome the data scarcity limitations and unlock the potential of deep learning based solutions. In our study we explore a range of data augmentation techniques from geometric transformations of wound images to advanced GANs, to enrich and expand datasets. Using the Keras, Tensorflow, and Pandas libraries, we implemented the data augmentation techniques that can generate realistic wound images. We show that geometric data augmentation can improve classification performance, F1 scores, by up to 11% on top of state-of-the-art models, across several key classes of wounds. Our experiments with GAN based augmentation prove the viability of using DE-GANs to generate wound images with richer variations. Our study and results show that data augmentation is a valuable privacy-preserving tool with huge potential to overcome the data scarcity limitations and we believe it will be part of any real-world ML-based wound care system.

本研究针对创伤分类中的数据稀缺问题，探讨了数据增强技术的应用，以提升深度学习模型的训练效果。通过几何变换和先进的生成对抗网络（GAN），我们证明了这些技术能显著提高分类性能，尤其在多个关键创伤类别中的F1分数提高了最高达11%。此研究突出了数据增强在医疗领域的潜力，特别是在实际的基于机器学习的创伤护理系统中的应用价值。

利用深度学习的数据增强技术研究以克服创伤分类中的数据稀缺