Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays.
Comput Methods Programs Biomed
; 196: 105608, 2020 Nov.
Article
in English
| MEDLINE | ID: covidwho-610088
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 ANDCONCLUSION:
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.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Radiography, Thoracic
/
Coronavirus Infections
/
Deep Learning
Type of study:
Diagnostic study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Comput Methods Programs Biomed
Journal subject:
Medical Informatics
Year:
2020
Document Type:
Article
Affiliation country:
J.cmpb.2020.105608
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