Goyal Manu, Reeves Neil D, Rajbhandari Satyan, Ahmad Naseer, Wang Chuan, Yap Moi Hoon
Centre for Advanced Computational Sciences,Faculty of Science and Engineering, Manchester Metropolitan University, M1 5GD, Manchester, UK.
Research Centre for Musculoskeletal Science & Sports Medicine, Faculty of Science and Engineering, Manchester Metropolitan University, M1 5GD, Manchester, UK.
Comput Biol Med. 2020 Feb;117:103616. doi: 10.1016/j.compbiomed.2020.103616. Epub 2020 Jan 10.
Recognition and analysis of Diabetic Foot Ulcers (DFU) using computerized methods is an emerging research area with the evolution of image-based machine learning algorithms. Existing research using visual computerized methods mainly focuses on recognition, detection, and segmentation of the visual appearance of the DFU as well as tissue classification. According to DFU medical classification systems, the presence of infection (bacteria in the wound) and ischaemia (inadequate blood supply) has important clinical implications for DFU assessment, which are used to predict the risk of amputation. In this work, we propose a new dataset and computer vision techniques to identify the presence of infection and ischaemia in DFU. This is the first time a DFU dataset with ground truth labels of ischaemia and infection cases is introduced for research purposes. For the handcrafted machine learning approach, we propose a new feature descriptor, namely the Superpixel Colour Descriptor. Then we use the Ensemble Convolutional Neural Network (CNN) model for more effective recognition of ischaemia and infection. We propose to use a natural data-augmentation method, which identifies the region of interest on foot images and focuses on finding the salient features existing in this area. Finally, we evaluate the performance of our proposed techniques on binary classification, i.e. ischaemia versus non-ischaemia and infection versus non-infection. Overall, our method performed better in the classification of ischaemia than infection. We found that our proposed Ensemble CNN deep learning algorithms performed better for both classification tasks as compared to handcrafted machine learning algorithms, with 90% accuracy in ischaemia classification and 73% in infection classification.
随着基于图像的机器学习算法的发展，使用计算机化方法识别和分析糖尿病足溃疡（DFU）是一个新兴的研究领域。现有的使用视觉计算机化方法的研究主要集中在DFU视觉外观的识别、检测和分割以及组织分类上。根据DFU医学分类系统，感染（伤口中的细菌）和缺血（血液供应不足）的存在对DFU评估具有重要的临床意义，可用于预测截肢风险。在这项工作中，我们提出了一个新的数据集和计算机视觉技术来识别DFU中感染和缺血的存在。这是首次引入具有缺血和感染病例真实标签的DFU数据集用于研究目的。对于手工制作的机器学习方法，我们提出了一种新的特征描述符，即超像素颜色描述符。然后我们使用集成卷积神经网络（CNN）模型来更有效地识别缺血和感染。我们提出使用一种自然数据增强方法，该方法识别足部图像上的感兴趣区域，并专注于找到该区域中存在的显著特征。最后，我们在二元分类上评估我们提出的技术的性能，即缺血与非缺血以及感染与非感染。总体而言，我们的方法在缺血分类方面比感染分类表现更好。我们发现，与手工制作的机器学习算法相比，我们提出的集成CNN深度学习算法在这两个分类任务中表现更好，缺血分类准确率为90%，感染分类准确率为73%。
