Diabetes mellitus (DM) can cause chronic foot issues and severe infections, including Diabetic Foot Ulcers (DFUs) that heal slowly due to insufficient blood flow. A recurrence of these ulcers can lead to 84% of lower limb amputations and even cause death. High-risk diabetes patients require expensive medications, regular check-ups, and proper personal hygiene to prevent DFUs, which affect 15–25% of diabetics. Accurate diagnosis, appropriate care, and prompt response can prevent amputations and fatalities through early and reliable DFU detection from image analysis. We propose a comprehensive deep learning-based system for detecting DFUs from patients’ feet images by reliably localizing ulcer points. Our method utilizes innovative model ensemble techniques—non-maximum suppression (NMS), Soft-NMS, and weighted bounding box fusion (WBF)—to combine predictions from state-of-the-art object detection models. The performances of diverse cutting-edge model architectures used in this study complement each other, leading to more generalized and improved results when combined in an ensemble. Our WBF-based approach combining YOLOv8m and FRCNN-ResNet101 achieves a mean average precision (mAP) score of 86.4% at the IoU threshold of 0.5 on the DFUC2020 dataset, significantly outperforming the former benchmark by 12.4%. We also perform external validation on the IEEE DataPort Diabetic Foot dataset which has demonstrated robust and reliable model performance on the qualitative analysis. In conclusion, our study effectively developed an innovative diabetic foot ulcer (DFU) detection system using an ensemble model of deep neural networks (DNNs). This AI-driven tool serves as an initial screening aid for medical professionals, augmenting the diagnostic process by enhancing sensitivity to potential DFU cases. While recognizing the presence of false positives, our research contributes to improving patient care through the integration of human medical expertise with AI-based solutions in DFU management.

糖尿病 (DM) 可导致慢性足部问题和严重感染，包括因血流不足而愈合缓慢的糖尿病足溃疡 (DFU)。这些溃疡的复发可导致84%的下肢截肢，甚至导致死亡。高危糖尿病患者需要昂贵的药物、定期检查和适当的个人卫生来预防 DFU，DFU 影响着 15-25% 的糖尿病患者。通过图像分析进行早期、可靠的 DFU 检测，准确的诊断、适当的护理和及时的反应可以防止截肢和死亡。我们提出了一种基于深度学习的综合系统，通过可靠地定位溃疡点来从患者足部图像中检测 DFU。我们的方法利用创新的模型集成技术——非极大值抑制 (NMS)、Soft-NMS 和加权边界框融合 (WBF)——来结合最先进的对象检测模型的预测。本研究中使用的各种尖端模型架构的性能相互补充，当组合在一个整体中时，可以产生更通用和改进的结果。我们基于 WBF 的方法结合了 YOLOv8m 和 FRCNN-ResNet101，在 DFUC2020 数据集上的 IoU 阈值为 0.5 时，平均精度 (mAP) 得分为 86.4%，显着优于之前的基准 12.4%。我们还对 IEEE DataPort 糖尿病足数据集进行了外部验证，该数据集在定性分析中展示了稳健可靠的模型性能。总之，我们的研究使用深度神经网络（DNN）的集成模型有效地开发了一种创新的糖尿病足溃疡（DFU）检测系统。这种人工智能驱动的工具可作为医疗专业人员的初步筛查辅助工具，通过提高对潜在 DFU 病例的敏感性来增强诊断过程。在认识到误报存在的同时，我们的研究通过将人类医疗专业知识与 DFU 管理中基于人工智能的解决方案相结合，有助于改善患者护理。