This research conducts an investigation on the effect of visually similar images within a publicly available diabetic foot ulcer dataset when training deep learning classification networks. The presence of binary-identical duplicate images in datasets used to train deep learning algorithms is a well known issue that can introduce unwanted bias which can degrade network performance. However, the effect of visually similar non-identical images is an under-researched topic, and has so far not been investigated in any diabetic foot ulcer studies. We use an open-source fuzzy algorithm to identify groups of increasingly similar images in the Diabetic Foot Ulcers Challenge 2021 (DFUC2021) training dataset. Based on each similarity threshold, we create new training sets that we use to train a range of deep learning multi-class classifiers. We then evaluate the performance of the best performing model on the DFUC2021 test set. Our findings show that the model trained on the training set with the 80\% similarity threshold images removed achieved the best performance using the InceptionResNetV2 network. This model showed improvements in F1-score, precision, and recall of 0.023, 0.029, and 0.013, respectively. These results indicate that highly similar images can contribute towards the presence of performance degrading bias within the Diabetic Foot Ulcers Challenge 2021 dataset, and that the removal of images that are 80\% similar from the training set can help to boost classification performance.

翻译：本研究针对公开的糖尿病足溃疡数据集中视觉相似图像对深度学习分类网络训练的影响展开调查。在用于训练深度学习算法的数据集中，存在二进制相同的重复图像是众所周知的问题，这可能会引入不良偏差，从而降低网络性能。然而，视觉相似但非完全相同的图像的影响是一个研究不足的课题，迄今为止尚未在任何糖尿病足溃疡研究中得到探讨。我们使用开源模糊算法在糖尿病足溃疡挑战赛2021（DFUC2021）训练数据集中识别出相似度逐渐增加的图像组。基于每个相似度阈值，我们创建了新的训练集，用于训练一系列深度学习多类分类器。随后，我们在DFUC2021测试集上评估了最佳性能模型的表现。我们的研究结果表明，在去除80%相似度阈值图像的训练集上训练的模型，在使用InceptionResNetV2网络时取得了最佳性能。该模型的F1分数、精确率和召回率分别提升了0.023、0.029和0.013。这些结果表明，高度相似的图像可能导致糖尿病足溃疡挑战赛2021数据集中存在降低性能的偏差，而移除训练集中80%相似的图像有助于提升分类性能。