利用红外热成像技术非接触式估算褥疮大小

IF 2.2 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Bhaskar Pandey;Ajat Shatru Arora;Deepak Joshi
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引用次数: 0

摘要

压迫性损伤会造成不适,并可能导致死亡,这凸显了伤口评估的重要性。在后 COVID 时代,对伤口进行远程监测,特别是通过红外热成像和深度学习等非接触式方法进行监测,势在必行。这封信提出了一种从热图像中进行维度检测的深度学习方法,并对来自 18 个受试者的数据进行了训练。实例分割的最高准确率达到 0.9542,分类准确率达到 0.9922。该模型对测量尺寸的均方根误差(RMSE)为 0.1609 厘米,在检测伤口长度(RMSE:0.1114 厘米)方面的准确性优于宽度(RMSE:0.1506 厘米)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Noncontact Size Estimation of Pressure Ulcers Using IR Thermal Imaging
Pressure injuries cause discomfort and potential fatality, underscoring the importance of wound assessment. In the post-COVID era, remote monitoring of wounds, particularly through noncontact methods like infrared (IR) thermal imaging and deep learning, is imperative. This letter proposes a deep learning approach for dimension detection from thermal images, trained on data from 18 subjects. Instance segmentation achieved a maximum accuracy of 0.9542, with classification accuracy reaching 0.9922. The model exhibited a root mean square error (RMSE) of 0.1609 cm for measured dimensions, with superior accuracy in detecting wound length (RMSE: 0.1114 cm) compared to width (RMSE: 0.1506 cm).
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来源期刊
IEEE Sensors Letters
IEEE Sensors Letters Engineering-Electrical and Electronic Engineering
CiteScore
3.50
自引率
7.10%
发文量
194
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