用图像和位置信息，精准分类四种常见伤口类型。
Multi-modal wound classification using wound image and location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)
- 融合Xception与高斯混合循环网络，结合图像与位置特征进行多模态分析。
- 在多种实验中，伤口分类准确率达78.77%至100%。
- 适合临床辅助诊断，尤其适用于糖尿病、压疮等慢性伤口识别。
急性及难愈性伤口的有效诊断对护理实践至关重要。不良临床结局常与感染、外周血管疾病及伤口加深相关，共同加重合并症。然而，基于人工智能（AI）的诊断工具可加速医学影像解读，提升疾病早期发现能力。本文提出一种基于迁移学习的多模态AI模型，结合Xception与高斯混合循环神经网络（GMRNN），通过融合迁移学习提取的特征与伤口位置信息，实现对糖尿病、压疮、手术及静脉性溃疡四类常见伤口的分类。该方法与医疗图像分析中的深度神经网络（DNN）进行全面对比。实验结果表明，在不同条件下，伤口分类准确率在78.77%至100%之间。研究结果展示了所提方法在利用伤口图像及其对应位置信息进行精准分类方面的卓越性能。
原文摘要 · Abstract (English)
The effective diagnosis of acute and hard-to-heal wounds is crucial for wound care practitioners to provide effective patient care. Poor clinical outcomes are often linked to infection, peripheral vascular disease, and increasing wound depth, which collectively exacerbate these comorbidities. However, diagnostic tools based on Artificial Intelligence (AI) speed up the interpretation of medical images and improve early detection of disease. In this article, we propose a multi-modal AI model based on transfer learning (TL), which combines two state-of-the-art architectures, Xception and GMRNN, for wound classification. The multi-modal network is developed by concatenating the features extracted by a transfer learning algorithm and location features to classify the wound types of diabetic, pressure, surgical, and venous ulcers. The proposed method is comprehensively compared with deep neural networks (DNN) for medical image analysis. The experimental results demonstrate a notable wound-class classifications (containing only diabetic, pressure, surgical, and venous) vary from 78.77 to 100\% in various experiments. The results presented in this study showcase the exceptional accuracy of the proposed methodology in accurately classifying the most commonly occurring wound types using wound images and their corresponding locations.
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