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

Enzo Tartaglione, Carlo Alberto Barbano, Claudio Berzovini, Marco Calandri and Marco Grangetto
Additional contact information
Enzo Tartaglione: Computer Science Department, University of Turin, 10149 Torino, Italy
Carlo Alberto Barbano: Computer Science Department, University of Turin, 10149 Torino, Italy
Claudio Berzovini: Azienda Ospedaliera Città della Salute e della Scienza Presidio Molinette, 10126 Torino, Italy
Marco Calandri: Oncology Department, University of Turin, AOU San Luigi Gonzaga, 10043 Orbassano, Italy
Marco Grangetto: Computer Science Department, University of Turin, 10149 Torino, Italy

IJERPH, 2020, vol. 17, issue 18, 1-17

Abstract: 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.

Keywords: chest X-ray; deep learning; classification; COVID-19 (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1660-4601/17/18/6933/pdf (application/pdf)
https://www.mdpi.com/1660-4601/17/18/6933/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:17:y:2020:i:18:p:6933-:d:417487

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().