Department of Dermatology, University Hospital Erlangen, Erlangen, Germany.
Health Informatics Research Group, Osnabrück University of AS., Germany.
Stud Health Technol Inform. 2024 Aug 30;317:347-355. doi: 10.3233/SHTI240877.
This study aims to advance the field of digital wound care by developing and evaluating convolutional neural network (CNN) architectures for the automatic classification of maceration, a significant wound healing complication, in 458 annotated wound images. Detection and classification of maceration can improve patient outcomes. Several CNN models were compared and MobileNetV2 emerged as the top-performing model, achieving the highest accuracy despite having fewer parameters. This finding underscores the importance of considering model complexity relative to dataset size. The study also explored the role of image cropping and the use of Grad-CAM visualizations to understand the decision-making process of the CNN. From a medical perspective, results indicate that employing CNNs for classification of maceration may enhance diagnostic accuracy and reduce the clinicians' time and effort.
本研究旨在通过开发和评估卷积神经网络（CNN）架构，为 458 张标注伤口图像中的浸渍自动分类这一重大伤口愈合并发症做出贡献，以推动数字伤口护理领域的发展。浸渍的检测和分类可以改善患者的预后。我们比较了几种 CNN 模型，结果表明 MobileNetV2 是表现最佳的模型，尽管它的参数较少，但却实现了最高的准确率。这一发现强调了相对于数据集大小考虑模型复杂性的重要性。该研究还探讨了图像裁剪的作用以及使用 Grad-CAM 可视化来了解 CNN 的决策过程。从医学角度来看，结果表明，使用 CNN 进行浸渍分类可能会提高诊断的准确性，并减少临床医生的时间和精力。
