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Cited by 623COVID-19 diagnosis using AIArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays

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Brunese, Luca, Mercaldo, Francesco, Reginelli, Alfonso, Santone, Antonella·2020·Computer Methods and Programs in Biomedicine
DOI 10.1016/j.cmpb.2020.105608PMC PMC7831868
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Abstract

Background and objective Coronavirus disease (COVID-19) is an infectious disease caused by a new virus never identified before in humans. This virus causes respiratory disease (for instance, flu) with symptoms such as cough, fever and, in severe cases, pneumonia. The test to detect the presence of this virus in humans is performed on sputum or blood samples and the outcome is generally available within a few hours or, at most, days. Analysing biomedical imaging the patient shows signs of pneumonia. In this paper, with the aim of providing a fully automatic and faster diagnosis, we propose the adoption of deep learning for COVID-19 detection from X-rays. Method In particular, we propose an approach composed by three phases: the first one to detect if in a chest X-ray there is the presence of a pneumonia. The second one to discern between COVID-19 and pneumonia. The last step is aimed to localise the areas in the X-ray symptomatic of the COVID-19 presence. Results and conclusion Experimental analysis on 6,523 chest X-rays belonging to different institutions demonstrated the effectiveness of the proposed approach, with an average time for COVID-19 detection of approximately 2.5 seconds and an average accuracy equal to 0.97.

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Sorted by citations

Authors
Brunese, L., Mercaldo, F., Reginelli, A., & Santone, A.
Affiliations
  • University of Molise (IT)
  • University of Campania "Luigi Vanvitelli" (IT)
Corresponding author
Mercaldo, Francesco
ORCID
Venue
Computer Methods and Programs in Biomedicine
Publisher
Elsevier BV
Published
2020-11
Volume
196
Pages
105608
ISSN
0169-2607 (ISSN-L)
DOI
10.1016/j.cmpb.2020.105608
PMC
PMC7831868
OpenAlex
W3036552116
Cited by
623
References
5
Citations by year
2026: 20 · 2025: 48 · 2024: 67 · 2023: 116 · 2022: 158 · 2021: 187 · 2020: 27
FWCI
59.42
Type
journal article
Language
en
Open access
Licence
Locations
  • Computer Methods and Programs in Biomedicine (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Archivio Istituzionale della Ricerca - Università degli Studi della Campania "Luigi Vanvitelli" (submittedVersion)
  • Archivio Istituzionale della Ricerca - Università degli Studi del Molise (submittedVersion)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
COVID-19 detection, chest radiography, pneumonia detection, explainable deep learning, lesion localization, automated diagnosis, lung disease
MeSH terms
Betacoronavirus, Deep Learning, COVID-19, SARS-CoV-2, Algorithms, Humans, Image Processing, Computer-Assisted, Lung Diseases, Pneumonia, Viral, Radiographic Image Interpretation, Computer-Assisted, Radiography, Thoracic, X-Rays, Reproducibility of Results, Neural Networks, Computer, Coronavirus Infections, Pandemics

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