Wang Jyun-Guo, Huang Yu-Ting
The Department of Medical Informatics, Tzu Chi University, Hualien County, Taiwan.
Phys Eng Sci Med. 2024 Dec;47(4):1581-1592. doi: 10.1007/s13246-024-01472-3. Epub 2024 Sep 2.
Diabetic foot ulcer (DFU) is a common chronic complication of diabetes. This complication is characterized by the formation of ulcers that are difficult to heal on the skin of the foot. Ulcers can negatively affect patients' quality of life, and improperly treated lesions can result in amputation and even death. Traditionally, the severity and type of foot ulcers are determined by doctors through visual observations and on the basis of their clinical experience; however, this subjective evaluation can lead to misjudgments. In addition, quantitative methods have been developed for classifying and scoring are therefore time-consuming and labor-intensive. In this paper, we propose a reconstruction residual network with a fused spatial-channel attention mechanism (FARRNet) for automatically classifying DFUs. The use of pseudo-labeling and Data augmentation as a pre-processing technique can overcome problems caused by data imbalance and small sample size. The developed model's attention was enhanced using a spatial channel attention (SPCA) module that incorporates spatial and channel attention mechanisms. A reconstruction mechanism was incorporated into the developed residual network to improve its feature extraction ability for achieving better classification. The performance of the proposed model was compared with that of state-of-the-art models and those in the DFUC Grand Challenge. When applied to the DFUC Grand Challenge, the proposed method outperforms other state-of-the-art schemes in terms of accuracy, as evaluated using 5-fold cross-validation and the following metrics: macro-average F1-score, AUC, Recall, and Precision. FARRNet achieved the F1-score of 60.81%, AUC of 87.37%, Recall of 61.04%, and Precision of 61.56%. Therefore, the proposed model is more suitable for use in medical diagnosis environments with embedded devices and limited computing resources. The proposed model can assist patients in initial identifications of ulcer wounds, thereby helping them to obtain timely treatment.
糖尿病足溃疡（DFU）是糖尿病常见的慢性并发症。这种并发症的特征是足部皮肤形成难以愈合的溃疡。溃疡会对患者的生活质量产生负面影响，治疗不当的病变可能导致截肢甚至死亡。传统上，医生通过视觉观察并基于临床经验来确定足部溃疡的严重程度和类型；然而，这种主观评估可能导致判断失误。此外，已经开发出用于分类和评分的定量方法，因此既耗时又费力。在本文中，我们提出了一种具有融合空间通道注意力机制的重建残差网络（FARRNet），用于自动对糖尿病足溃疡进行分类。使用伪标签和数据增强作为预处理技术可以克服数据不平衡和样本量小所带来的问题。通过结合空间和通道注意力机制的空间通道注意力（SPCA）模块增强了所开发模型的注意力。在所开发的残差网络中纳入了一种重建机制，以提高其特征提取能力，从而实现更好的分类。将所提出模型的性能与最先进的模型以及糖尿病足溃疡大挑战中的模型进行了比较。当应用于糖尿病足溃疡大挑战时，使用5折交叉验证和以下指标进行评估，所提出的方法在准确性方面优于其他最先进的方案：宏平均F1分数、AUC、召回率和精确率。FARRNet的F1分数达到60.81%，AUC为87.37%，召回率为61.04%，精确率为61.56%。因此，所提出的模型更适合在具有嵌入式设备和有限计算资源的医疗诊断环境中使用。所提出的模型可以帮助患者初步识别溃疡伤口，从而帮助他们获得及时治疗。
