Al-Garaawi Nora, Ebsim Raja, Alharan Abbas F H, Yap Moi Hoon
Department of Computer Science, Faculty of Education for Girls, University of Kufa, Najaf, Iraq.
Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester, UK.
Comput Biol Med. 2022 Jan;140:105055. doi: 10.1016/j.compbiomed.2021.105055. Epub 2021 Nov 24.
Diabetic foot ulcer (DFU) is a major complication of diabetes and can lead to lower limb amputation if not treated early and properly. In addition to the traditional clinical approaches, in recent years, research on automation using computer vision and machine learning methods plays an important role in DFU classification, achieving promising successes. The most recent automatic approaches to DFU classification are based on convolutional neural networks (CNNs), using solely RGB images as input. In this paper, we present a CNN-based DFU classification method in which we showed that feeding an appropriate feature (texture information) to the CNN model provides a complementary performance to the standard RGB-based deep models of the DFU classification task, and better performance can be obtained if both RGB images and their texture features are combined and used as input to the CNN. To this end, the proposed method consists of two main stages. The first stage extracts texture information from the RGB image using the mapped binary patterns technique. The obtained mapped image is used to aid the second stage in recognizing DFU as it contains texture information of ulcer. The stack of RGB and mapped binary patterns images are fed to the CNN as a tensor input or as a fused image, which is a linear combination of RGB and mapped binary patterns images. The performance of the proposed approach was evaluated using two recently published DFU datasets: the Part-A dataset of healthy and unhealthy (DFU) cases [17] and Part-B dataset of ischaemia and infection cases [18]. The results showed that the proposed methods provided better performance than the state-of-the-art CNN-based methods with 0.981% (AUC) and 0.952% (F-Measure) on the Part-A dataset, 0.995% (AUC) and 0.990% (F-measure) for the Part-B ischaemia dataset, and 0.820% (AUC) and 0.744% (F-measure) on the Part-B infection dataset.
糖尿病足溃疡（DFU）是糖尿病的一种主要并发症，如果不及早且妥善治疗，可能导致下肢截肢。除了传统的临床方法外，近年来，利用计算机视觉和机器学习方法进行自动化的研究在DFU分类中发挥了重要作用，并取得了令人瞩目的成果。最新的DFU分类自动方法基于卷积神经网络（CNN），仅使用RGB图像作为输入。在本文中，我们提出了一种基于CNN的DFU分类方法，我们证明向CNN模型输入适当的特征（纹理信息）可为DFU分类任务的基于标准RGB的深度模型提供互补性能，并且如果将RGB图像及其纹理特征组合并用作CNN的输入，则可获得更好的性能。为此，所提出的方法包括两个主要阶段。第一阶段使用映射二进制模式技术从RGB图像中提取纹理信息。获得的映射图像用于辅助第二阶段识别DFU，因为它包含溃疡的纹理信息。RGB图像和映射二进制模式图像的堆栈作为张量输入或作为融合图像（RGB图像和映射二进制模式图像的线性组合）输入到CNN。使用两个最近发布的DFU数据集对所提出方法的性能进行了评估：健康和不健康（DFU）病例的A部分数据集[17]以及缺血和感染病例的B部分数据集[18]。结果表明，所提出的方法比基于CNN的现有方法具有更好的性能，在A部分数据集上的AUC为0.981%，F-Measure为0.952%；在B部分缺血数据集上的AUC为0.995%，F-measure为0.990%；在B部分感染数据集上的AUC为0.820%，F-measure为0.744%。
