Khosa Ikramullah, Raza Awais, Anjum Mohd, Ahmad Waseem, Shahab Sana
Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan.
Department of Computer Engineering, Aligarh Muslim University, Aligarh 202002, India.
Diagnostics (Basel). 2023 Aug 10;13(16):2637. doi: 10.3390/diagnostics13162637.
Lower extremity diabetic foot ulcers (DFUs) are a severe consequence of diabetes mellitus (DM). It has been estimated that people with diabetes have a 15% to 25% lifetime risk of acquiring DFUs which leads to the risk of lower limb amputations up to 85% due to poor diagnosis and treatment. Diabetic foot develops planter ulcers where thermography is used to detect the changes in the planter temperature. In this study, publicly available thermographic image data including both control group and diabetic group patients are used. Thermograms at image level as well as patch level are utilized for DFU detection. For DFU recognition, several machine-learning-based classification approaches are employed with hand-crafted features. Moreover, a couple of convolutional neural network models including ResNet50 and DenseNet121 are evaluated for DFU recognition. Finally, a CNN-based custom-developed model is proposed for the recognition task. The results are produced using image-level data, patch-level data, and image-patch combination data. The proposed CNN-based model outperformed the utilized models as well as the state-of-the-art models in terms of the AUC and accuracy. Moreover, the recognition accuracy for both the machine-learning and deep-learning approaches was higher for the image-level thermogram data in comparison to the patch-level or combination of image-patch thermograms.
下肢糖尿病足溃疡（DFUs）是糖尿病（DM）的严重后果。据估计，糖尿病患者一生中患DFUs的风险为15%至25%，由于诊断和治疗不当，这会导致高达85%的下肢截肢风险。糖尿病足会出现足底溃疡，可通过热成像检测足底温度的变化。在本研究中，使用了包括对照组和糖尿病组患者的公开可用热成像图像数据。在图像级别以及斑块级别利用热成像图进行DFU检测。对于DFU识别，采用了几种基于机器学习的分类方法，并使用手工制作的特征。此外，还评估了包括ResNet50和DenseNet121在内的几种卷积神经网络模型用于DFU识别。最后，提出了一种基于卷积神经网络的定制开发模型用于识别任务。使用图像级数据、斑块级数据和图像 - 斑块组合数据得出结果。所提出的基于卷积神经网络的模型在AUC和准确率方面优于所使用的模型以及当前的先进模型。此外，与斑块级或图像 - 斑块热成像图组合相比，机器学习和深度学习方法对图像级热成像图数据的识别准确率更高。
