🤖 AI Summary
To address insufficient diagnostic accuracy for acute and chronic wounds in clinical practice, this paper proposes a multimodal intelligent classification method integrating wound images and anatomical location information. We introduce a novel collaborative architecture combining Xception for deep visual feature extraction and a Gaussian Mixture Recurrent Neural Network (GMRNN) — the first to explicitly model temporal semantic relationships among anatomical locations. Multimodal features are fused via concatenation and jointly optimized through end-to-end training, overcoming limitations of single-image modality. Evaluated on four wound types—diabetic, pressure, surgical, and venous ulcers—the method achieves classification accuracies ranging from 78.77% to 100%, significantly outperforming conventional deep learning models. This work pioneers the incorporation of anatomical location modeling into intelligent wound diagnosis, empirically validating the efficacy and clinical applicability of location-aware multimodal representation learning.
📝 Abstract
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.