Karthik R, Ajay Armaano, Jhalani Anshika, Ballari Kruthik, K Suganthi
Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India.
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India.
Sci Rep. 2025 Feb 3;15(1):4057. doi: 10.1038/s41598-025-87519-1.
Diabetic Foot Ulcer (DFU) is a severe complication of diabetes mellitus, resulting in significant health and socio-economic challenges for the diagnosed individual. Severe cases of DFU can lead to lower limb amputation in diabetic patients, making their diagnosis a complex and costly process that poses challenges for medical professionals. Manual identification of DFU is particularly difficult due to their diverse visual characteristics, leading to multiple cases going undiagnosed. To address this challenge, Deep Learning (DL) methods offer an efficient and automated approach to facilitate timely treatment and improve patient outcomes. This research proposes a novel feature fusion-based model that incorporates two parallel tracks for efficient feature extraction. The first track utilizes the Swin transformer, which captures long-range dependencies by employing shifted windows and self-attention mechanisms. The second track involves the Efficient Multi-Scale Attention-Driven Network (EMADN), which leverages Light-weight Multi-scale Deformable Shuffle (LMDS) and Global Dilated Attention (GDA) blocks to extract local features efficiently. These blocks dynamically adjust kernel sizes and leverage attention modules, enabling effective feature extraction. To the best of our knowledge, this is the first work reporting the findings of a dual track architecture for DFU classification, leveraging Swin transformer and EMADN networks. The obtained feature maps from both the networks are concatenated and subjected to shuffle attention for feature refinement at a reduced computational cost. The proposed work also incorporates Grad-CAM-based Explainable Artificial Intelligence (XAI) to visualize and interpret the decision making of the network. The proposed model demonstrated better performance on the DFUC-2021 dataset, surpassing existing works and pre-trained CNN architectures with an accuracy of 78.79% and a macro F1-score of 80%.
糖尿病足溃疡（DFU）是糖尿病的一种严重并发症，给确诊患者带来了重大的健康和社会经济挑战。严重的DFU病例可导致糖尿病患者下肢截肢，使其诊断成为一个复杂且成本高昂的过程，给医学专业人员带来了挑战。由于DFU具有多样的视觉特征，手动识别尤为困难，导致许多病例未被诊断出来。为应对这一挑战，深度学习（DL）方法提供了一种高效且自动化的途径，以促进及时治疗并改善患者预后。本研究提出了一种基于特征融合的新型模型，该模型包含两条并行路径以进行高效特征提取。第一条路径使用Swin变换器，它通过采用移动窗口和自注意力机制来捕捉长距离依赖关系。第二条路径涉及高效多尺度注意力驱动网络（EMADN），该网络利用轻量级多尺度可变形混洗（LMDS）和全局扩张注意力（GDA）模块来高效提取局部特征。这些模块动态调整内核大小并利用注意力模块，从而实现有效的特征提取。据我们所知，这是第一项报告利用Swin变换器和EMADN网络进行DFU分类的双路径架构研究结果的工作。从这两个网络获得的特征图被连接起来，并经过混洗注意力操作以降低计算成本进行特征细化。所提出的工作还纳入了基于Grad-CAM的可解释人工智能（XAI），以可视化和解释网络的决策过程。所提出的模型在DFUC-2021数据集上表现出更好的性能，以78.79%的准确率和80%的宏F1分数超过了现有工作和预训练的CNN架构。
