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Article

Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data

1
Computer Science Department, University of Turin, 10149 Torino, Italy
2
Azienda Ospedaliera Città della Salute e della Scienza Presidio Molinette, 10126 Torino, Italy
3
Oncology Department, University of Turin, AOU San Luigi Gonzaga, 10043 Orbassano, Italy
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(18), 6933; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17186933
Received: 11 August 2020 / Revised: 11 September 2020 / Accepted: 14 September 2020 / Published: 22 September 2020
(This article belongs to the Special Issue The COVID-19 Pandemic in Europe: Response to Challenges)
The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR. View Full-Text
Keywords: chest X-ray; deep learning; classification; COVID-19 chest X-ray; deep learning; classification; COVID-19
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MDPI and ACS Style

Tartaglione, E.; Barbano, C.A.; Berzovini, C.; Calandri, M.; Grangetto, M. Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data. Int. J. Environ. Res. Public Health 2020, 17, 6933. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17186933

AMA Style

Tartaglione E, Barbano CA, Berzovini C, Calandri M, Grangetto M. Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data. International Journal of Environmental Research and Public Health. 2020; 17(18):6933. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17186933

Chicago/Turabian Style

Tartaglione, Enzo, Carlo A. Barbano, Claudio Berzovini, Marco Calandri, and Marco Grangetto. 2020. "Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data" International Journal of Environmental Research and Public Health 17, no. 18: 6933. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17186933

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