目的

构建全局-局部特征分层融合图像分类网络模型，以提升烧烫伤创面深度评估的可靠性与准确性。

方法

收集就诊于上海交通大学医学院附属瑞金医院烧伤整形科门急诊烧烫伤患者创面图像共619张，由2名具有3年以上工作经验的烧伤科医师使用图像标注工具LabelMe对图像进行独立标注，并与科室其他医师进行交叉验证。按7∶2∶1的比例将图像集分为训练集、验证集与测试集，并对训练集进行数据扩充，共得到2 598张训练集图像。设计并构建全局-局部特征分层融合网络HFNet进行预训练，学习图像的基础特征后迁移到已收集的烧烫伤图像分类数据集，通过参数优化提升分类精度。将HFNet模型的查准率、召回率、F1指数和推理时间与ConvNeXt、Swin-Transformer、UniFormer、BiFormer模型进行对比，评估其性能。

结果

经测试，HFNet模型在Ⅰ度、Ⅱ度、Ⅲ度烧烫伤创面分类任务中的查准率分别为93.53%、94.08%和86.52%，均值为91.63%；召回率分别为91.99%、89.89%和92.71%，均值为91.69%；F1指数分别为93.56%、90.96%和90.46%，均值为91.66%；平均精度分别为92.75%、91.94%和89.51%，平均精度均值为91.40%。混淆矩阵显示，HFNet模型在Ⅰ度、Ⅱ度、Ⅲ度烧烫伤创面分类任务中的准确度分别为90%、92%和93%。与BiFormer等其他模型相比，HFNet模型查准率较高，推理速度中等，整体上在准确性与推理效率之间取得了良好平衡。

结论

HFNet模型应用于烧烫伤创面深度评估能提高其准确性和诊断效率，可为烧伤专科医师快速判断烧烫伤严重程度提供精准的分类信息；同时能够积累大量高质量的烧烫伤创面分类数据，为后续优化模型提供支持。

Objective

To establish a global-local feature hierarchical fusion image classification network model，improve the reliability and accuracy of burn and scald wound depth assessment.

Methods

A total of 619 wound images of burn and scald patients who were admitted to the Department of Burn and Plastic Surgery at Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine were collected.Two burn physicians with more than 3 years of work experience independently annotated the images using the image annotation tool LabelMe， and cross validated with other physicians in the department.The image set was divided into a training set， validation set， and test set in a ratio of 7∶2∶1， and the training set was expanded to obtain 2 598 images.Designed and constructed a global-local feature hierarchical fusion network HFNet for pre-training， learned the basic features of images and transferred them to the collected burn image classification dataset.Improved classification accuracy through parameter optimization and compared the precision， recall， F1 score， and inference time of the HFNet model with ConvNeXt， Swin-Transformer，UniFormer， and BiFormer models to evaluate its performance.

Results

Testing results showed that the precision of the HFNet model in the classification tasks of first-degree， second-degree， and third-degree burn wounds was 93.53%， 94.08%， and 86.52%， respectively， with a mean of 91.63%.The recall rates were 91.99%， 89.89%， and 92.71%， respectively， with a mean of 91.69%.The F1 index was 93.56%， 90.96%，and 90.46%， respectively， with a mean of 91.66%.The average accuracy was 92.75%， 91.94%， and 89.51%，respectively， with an average accuracy of 91.40%.The confusion matrix showed that the accuracy of the HFNet model in the classification tasks of first-degree， second-degree， and third-degree burn wounds was 90%，92%， and 93%， respectively.Compared to models such as BiFormer， the HFNet model achieved higher precision with moderate inference speed， striking a good overall balance between accuracy and computational efficiency.

Conclusion

The HFNet model enhances the accuracy and efficiency of burn depth assessment，providing burn specialists with precise classification information to rapidly determine the severity of burn injuries.Additionally， the model enables the accumulation of high-quality classification data， supporting further model optimization.