利用 Xception 和高斯混合循环神经网络（GMRNN）通过伤口图像与位置进行多模态伤口分类
Multi-modal wound classification using wound image and location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)
AI总结：
提出融合 Xception 图像迁移学习特征、GMRNN 与伤口位置特征的多模态模型，用于四类溃疡分类，实验准确率达 78.77%–100%。
摘要：
有效诊断急性伤口和难愈合伤口，对于伤口护理从业者提供有效的患者护理至关重要。不良临床结局通常与感染、外周血管疾病以及伤口深度增加有关，这些因素共同加剧了这些合并症。然而，基于人工智能（AI）的诊断工具可加快医学图像解读，并改善疾病的早期检测。本文提出一种基于迁移学习（TL）的多模态 AI 模型，该模型结合了 Xception 和 GMRNN 两种先进架构，用于伤口分类。该多模态网络通过将迁移学习算法提取的特征与位置特征拼接而构建，以对糖尿病性溃疡、压力性溃疡、手术性溃疡和静脉性溃疡的伤口类型进行分类。所提出方法与用于医学图像分析的深度神经网络（DNN）进行了全面比较。实验结果表明，在不同实验中，显著的伤口分类结果（仅包含糖尿病性、压力性、手术性和静脉性伤口）在 78.77% 至 100% 之间变化。本研究给出的结果展示了所提方法在利用伤口图像及其对应位置准确分类最常见伤口类型方面的卓越准确性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
