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

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

doi:10.1002/jbio.202300486

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

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

Tel Aviv University

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Journal	Journal of Biophotonics
DOIs	https://doi.org/10.1002/jbio.202300486
State	Accepted/In press - 2024

Keywords

machine learning
mobile health
pneumonia
thermal imaging
transfer learning

All Science Journal Classification (ASJC) codes

General Chemistry
General Materials Science
General Biochemistry,Genetics and Molecular Biology
General Engineering
General Physics and Astronomy

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1002/jbio.202300486

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Hoffer, O., Brzezinski, R. Y., Ganim, A., Shalom, P., Ovadia-Blechman, Z., Ben-Baruch, L., Lewis, N., Peled, R., Shimon, C., Naftali-Shani, N., Katz, E., Zimmer, Y., & Rabin, N. (Accepted/In press). Smartphone-based detection of COVID-19 and associated pneumonia using thermal imaging and a transfer learning algorithm. Journal of Biophotonics. https://doi.org/10.1002/jbio.202300486

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title = "Smartphone-based detection of COVID-19 and associated pneumonia using thermal imaging and a transfer learning algorithm",

abstract = "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.",

keywords = "machine learning, mobile health, pneumonia, thermal imaging, transfer learning",

author = "Oshrit Hoffer and Brzezinski, \{Rafael Y.\} and Adam Ganim and Perry Shalom and Zehava Ovadia-Blechman and Lital Ben-Baruch and Nir Lewis and Racheli Peled and Carmi Shimon and Nili Naftali-Shani and Eyal Katz and Yair Zimmer and Neta Rabin",

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year = "2024",

doi = "10.1002/jbio.202300486",

language = "الإنجليزيّة",

journal = "Journal of Biophotonics",

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TY - JOUR

T1 - Smartphone-based detection of COVID-19 and associated pneumonia using thermal imaging and a transfer learning algorithm

AU - Hoffer, Oshrit

AU - Brzezinski, Rafael Y.

AU - Ganim, Adam

AU - Shalom, Perry

AU - Ovadia-Blechman, Zehava

AU - Ben-Baruch, Lital

AU - Lewis, Nir

AU - Peled, Racheli

AU - Shimon, Carmi

AU - Naftali-Shani, Nili

AU - Katz, Eyal

AU - Zimmer, Yair

AU - Rabin, Neta

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - machine learning

KW - mobile health

KW - pneumonia

KW - thermal imaging

KW - transfer learning

UR - http://www.scopus.com/inward/record.url?scp=85182852819&partnerID=8YFLogxK

U2 - 10.1002/jbio.202300486

DO - 10.1002/jbio.202300486

M3 - مقالة

C2 - 38253344

SN - 1864-063X

JO - Journal of Biophotonics

JF - Journal of Biophotonics

ER -

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

Abstract

Keywords

All Science Journal Classification (ASJC) codes

UN SDGs

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