Understanding the appropriate skin layer thickness in wounded sites is an important tool to move forward on wound healing practices and treatment protocols. Methods to measure depth often are invasive and less specific. This paper introduces a novel method that is non-invasive with deep learning techniques using classifying of skin layers that helps in measurement of wound depth through heatmap analysis. A set of approximately 200 labeled images of skin allows five classes to be distinguished: scars, wounds, and healthy skin, among others. Each image has annotated key layers, namely the stratum cornetum, the epidermis, and the dermis, in the software Roboflow. In the preliminary stage, the Heatmap generator VGG16 was used to enhance the visibility of tissue layers, based upon which their annotated images were used to train ResNet18 with early stopping techniques. It ended up at a very high accuracy rate of 97.67%. To do this, the comparison of the models ResNet18, VGG16, DenseNet121, and EfficientNet has been done where both EfficientNet and ResNet18 have attained accuracy rates of almost 95.35%. For further hyperparameter tuning, EfficientNet and ResNet18 were trained at six different learning rates to determine the best model configuration. It has been noted that the accuracy has huge variations with different learning rates. In the case of EfficientNet, the maximum achievable accuracy was 95.35% at the rate of 0.0001. The same was true for ResNet18, which also attained its peak value of 95.35% at the same rate. These facts indicate that the model can be applied and utilized in actual-time, non-invasive wound assessment, which holds a great promise to improve clinical diagnosis and treatment planning.

翻译：准确评估创面皮肤层厚度是推动伤口愈合实践与治疗方案优化的重要工具。现有深度测量方法通常具有侵入性且特异性不足。本文提出一种基于深度学习皮肤层分类的非侵入性新方法，通过热图分析辅助伤口深度测量。利用约200张标注皮肤图像构建了包含疤痕、创面与健康皮肤等五类样本的数据集。所有图像均在Roboflow软件中标注了角质层、表皮层和真皮层等关键层结构。在预处理阶段，采用热图生成器VGG16增强组织层可视性，并基于标注图像通过早停法训练ResNet18模型，最终获得97.67%的极高准确率。通过对比ResNet18、VGG16、DenseNet121和EfficientNet模型性能，发现EfficientNet与ResNet18均达到约95.35%的准确率。进一步对这两种模型进行六种学习率的超参数调优，结果显示准确率随学习率变化显著波动。EfficientNet在0.0001学习率下取得95.35%的最高准确率，ResNet18在同等学习率下也达到95.35%的峰值性能。研究表明，该模型具备实时非侵入式创面评估的应用潜力，为临床诊断与治疗规划提供了重要技术支撑。