Department of Electrical Engineering, Qatar University, Doha 2713, Qatar.
Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600 UKM, Selangor, Malaysia.
Sensors (Basel). 2022 Feb 24;22(5):1793. doi: 10.3390/s22051793.
Diabetes mellitus (DM) can lead to plantar ulcers, amputation and death. Plantar foot thermogram images acquired using an infrared camera have been shown to detect changes in temperature distribution associated with a higher risk of foot ulceration. Machine learning approaches applied to such infrared images may have utility in the early diagnosis of diabetic foot complications. In this work, a publicly available dataset was categorized into different classes, which were corroborated by domain experts, based on a temperature distribution parameter-the thermal change index (TCI). We then explored different machine-learning approaches for classifying thermograms of the TCI-labeled dataset. Classical machine learning algorithms with feature engineering and the convolutional neural network (CNN) with image enhancement techniques were extensively investigated to identify the best performing network for classifying thermograms. The multilayer perceptron (MLP) classifier along with the features extracted from thermogram images showed an accuracy of 90.1% in multi-class classification, which outperformed the literature-reported performance metrics on this dataset.
糖尿病（DM）可导致足底溃疡、截肢和死亡。使用红外摄像机获取的足底热图图像已被证明可检测与足底溃疡风险较高相关的温度分布变化。应用于此类红外图像的机器学习方法可能对糖尿病足并发症的早期诊断有用。在这项工作中，根据温度分布参数-热变化指数（TCI），我们将一个公开的数据集分为不同的类别，这些类别是由领域专家根据 TCI 进行标记的。然后，我们探索了不同的机器学习方法来对 TCI 标记数据集的热图进行分类。我们广泛研究了具有特征工程的经典机器学习算法和具有图像增强技术的卷积神经网络（CNN），以确定用于对热图进行分类的最佳表现网络。多层感知机（MLP）分类器以及从热图图像中提取的特征在多类分类中表现出 90.1%的准确率，优于该数据集的文献报告的性能指标。
