利用热成像和迁移学习算法,基于智能手机检测 COVID-19 和相关肺炎。

Oshrit Hoffer, Rafael Y Brzezinski, Adam Ganim, Perry Shalom, Zehava Ovadia-Blechman, Lital Ben-Baruch, Nir Lewis, Racheli Peled, Carmi Shimon, Nili Naftali-Shani, Eyal Katz, Yair Zimmer, Neta Rabin
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摘要

COVID-19 相关肺炎通常通过胸部 X 光或计算机断层扫描图像进行诊断。然而,这些技术只能在医院使用。相比之下,红外热像仪是一种可与智能手机连接的便携式廉价设备。因此,它们可用于检测和监控医院外的医疗状况。在此,我们开发了一款基于智能手机的应用,利用人体背部的热图像进行 COVID-19 检测。使用深度学习算法进行的图像分析显示,灵敏度和特异度分别为 88.7% 和 92.3%。研究结果支持未来将无创热成像用于 COVID-19 和相关肺炎的初级筛查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Smartphone-based detection of COVID-19 and associated pneumonia using thermal imaging and a transfer learning algorithm.

COVID-19-related pneumonia is typically diagnosed using chest x-ray or computed tomography images. However, these techniques can only be used in hospitals. In contrast, thermal cameras are portable, inexpensive devices that can be connected to smartphones. Thus, they can be used to detect and monitor medical conditions outside hospitals. Herein, a smartphone-based application using thermal images of a human back was developed for COVID-19 detection. Image analysis using a deep learning algorithm revealed a sensitivity and specificity of 88.7% and 92.3%, respectively. The findings support the future use of noninvasive thermal imaging in primary screening for COVID-19 and associated pneumonia.

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