🤖 AI Summary
Chronic wounds (e.g., diabetic foot ulcers) suffer from subjective clinical assessment and inconsistent classification, leading to delayed interventions. To address this, we propose an Internet-of-Medical-Things (IoMT) system for intelligent chronic wound management. Our method introduces the first weighted multimodal architecture integrating ResNet-50, self-supervised DINOv2 Vision Transformer, and Swin Transformer, coupled with a longitudinal healing tracking mechanism enabling automatic six-class wound classification and dynamic healing quantification—including healing rate estimation and early anomaly detection. The system leverages self-supervised pretraining, multi-scale feature modeling, and edge–cloud collaborative deployment. Evaluated on a dataset of 5,175 multi-etiology wound images, it achieves 99.90% classification accuracy—surpassing state-of-the-art methods by 3.7%. It supports real-time healing scoring, severity quantification, and clinically actionable alerts.
📝 Abstract
Chronic wounds, including diabetic foot ulcers which affect up to one-third of people with diabetes, impose a substantial clinical and economic burden, with U.S. healthcare costs exceeding 25 billion dollars annually. Current wound assessment remains predominantly subjective, leading to inconsistent classification and delayed interventions. We present WoundNet-Ensemble, an Internet of Medical Things system leveraging a novel ensemble of three complementary deep learning architectures: ResNet-50, the self-supervised Vision Transformer DINOv2, and Swin Transformer, for automated classification of six clinically distinct wound types. Our system achieves 99.90 percent ensemble accuracy on a comprehensive dataset of 5,175 wound images spanning diabetic foot ulcers, pressure ulcers, venous ulcers, thermal burns, pilonidal sinus wounds, and fungating malignant tumors. The weighted fusion strategy demonstrates a 3.7 percent improvement over previous state-of-the-art methods. Furthermore, we implement a longitudinal wound healing tracker that computes healing rates, severity scores, and generates clinical alerts. This work demonstrates a robust, accurate, and clinically deployable tool for modernizing wound care through artificial intelligence, addressing critical needs in telemedicine and remote patient monitoring. The implementation and trained models will be made publicly available to support reproducibility.