DFU is a severe complication of diabetes that can lead to amputation of the lower limb if not treated properly. Inspired by the 2021 Diabetic Foot Ulcer Grand Challenge, researchers designed automated multi-class classification of DFU, including infection, ischaemia, both of these conditions, and none of these conditions. However, it remains a challenge as classification accuracy is still not satisfactory. This paper proposes a Venn Diagram interpretation of multi-label CNN-based method, utilizing different image enhancement strategies, to improve the multi-class DFU classification. We propose to reduce the four classes into two since both class wounds can be interpreted as the simultaneous occurrence of infection and ischaemia and none class wounds as the absence of infection and ischaemia. We introduce a novel Venn Diagram representation block in the classifier to interpret all four classes from these two classes. To make our model more resilient, we propose enhancing the perceptual quality of DFU images, particularly blurry or inconsistently lit DFU images, by performing color and sharpness enhancements on them. We also employ a fine-tuned optimization technique, adaptive sharpness aware minimization, to improve the CNN model generalization performance. The proposed method is evaluated on the test dataset of DFUC2021, containing 5,734 images and the results are compared with the top-3 winning entries of DFUC2021. Our proposed approach outperforms these existing approaches and achieves Macro-Average F1, Recall and Precision scores of 0.6592, 0.6593, and 0.6652, respectively.Additionally, We perform ablation studies and image quality measurements to further interpret our proposed method. This proposed method will benefit patients with DFUs since it tackles the inconsistencies in captured images and can be employed for a more robust remote DFU wound classification.

翻译：糖尿病足溃疡（DFU）是糖尿病的严重并发症，若处理不当可能导致下肢截肢。受2021年糖尿病足溃疡挑战赛启发，研究人员设计了DFU的自动多类分类，包括感染、缺血、两者兼具以及两者皆无四类。然而，由于分类准确率仍不理想，该问题依然具有挑战性。本文提出一种基于维恩图解释的多标签CNN方法，利用不同图像增强策略来改善DFU多类分类。我们将四类简化为两类，因为既有类伤口可解释为感染和缺血同时发生，而无症状类伤口可解释为感染和缺血均不存在。我们在分类器中引入新颖的维恩图表示模块，从这两类中解释所有四类。为增强模型鲁棒性，我们通过颜色和清晰度增强来提升DFU图像的感知质量，特别针对模糊或光照不一致的图像。我们还采用微调优化技术——自适应锐度感知最小化，以提高CNN模型的泛化性能。该方法在包含5,734张图像的DFUC2021测试数据集上评估，并与DFUC2021前三名优胜结果进行比较。我们提出的方法优于这些现有方法，宏平均F1分数、召回率和精确率分别达到0.6592、0.6593和0.6652。此外，我们通过消融研究和图像质量测量进一步解释所提方法。该方法通过解决采集图像的不一致性问题，可用于更稳健的远程DFU伤口分类，从而造福DFU患者。