The global burden of acute and chronic wounds presents a compelling case for enhancing wound classification methods, a vital step in diagnosing and determining optimal treatments. Recognizing this need, we introduce an innovative multi-modal network based on a deep convolutional neural network for categorizing wounds into four categories: diabetic, pressure, surgical, and venous ulcers. Our multi-modal network uses wound images and their corresponding body locations for more precise classification. A unique aspect of our methodology is incorporating a body map system that facilitates accurate wound location tagging, improving upon traditional wound image classification techniques. A distinctive feature of our approach is the integration of models such as VGG16, ResNet152, and EfficientNet within a novel architecture. This architecture includes elements like spatial and channel-wise Squeeze-and-Excitation modules, Axial Attention, and an Adaptive Gated Multi-Layer Perceptron, providing a robust foundation for classification. Our multi-modal network was trained and evaluated on two distinct datasets comprising relevant images and corresponding location information. Notably, our proposed network outperformed traditional methods, reaching an accuracy range of 74.79% to 100% for Region of Interest (ROI) without location classifications, 73.98% to 100% for ROI with location classifications, and 78.10% to 100% for whole image classifications. This marks a significant enhancement over previously reported performance metrics in the literature. Our results indicate the potential of our multi-modal network as an effective decision-support tool for wound image classification, paving the way for its application in various clinical contexts.

翻译：急性和慢性伤口的全球负担凸显了改进伤口分类方法的重要性，这是诊断和确定最佳治疗方案的关键步骤。基于这一需求，我们引入了一种基于深度卷积神经网络的新型多模态网络，用于将伤口分为四类：糖尿病溃疡、压力性溃疡、手术伤口和静脉溃疡。我们的多模态网络利用伤口图像及其对应的身体位置实现更精确的分类。该方法的一个独特之处在于引入了身体地图系统，便于准确的伤口位置标注，从而改进了传统的伤口图像分类技术。我们方法的另一个显著特征是在新型架构中集成了VGG16、ResNet152和EfficientNet等模型。该架构包含空间和通道维度的Squeeze-and-Excitation模块、轴向注意力机制以及自适应门控多层感知器，为分类提供了坚实的基础。我们的多模态网络在两个包含相关图像及对应位置信息的独立数据集上进行了训练和评估。值得注意的是，所提出的网络优于传统方法，在无位置分类的兴趣区域（ROI）分类中达到74.79%至100%的准确率，在有位置分类的ROI中达到73.98%至100%，在全图像分类中达到78.10%至100%。这显著优于文献中先前报道的性能指标。结果表明，我们的多模态网络有望成为伤口图像分类的有效决策支持工具，为其在多种临床场景中的应用铺平了道路。