Explainable DCNN based chest X-ray image analysis and classification for COVID-19 pneumonia detection.
Sci Rep
; 11(1): 16071, 2021 08 09.
Article
in English
| MEDLINE | ID: covidwho-1349689
ABSTRACT
To speed up the discovery of COVID-19 disease mechanisms by X-ray images, this research developed a new diagnosis platform using a deep convolutional neural network (DCNN) that is able to assist radiologists with diagnosis by distinguishing COVID-19 pneumonia from non-COVID-19 pneumonia in patients based on chest X-ray classification and analysis. Such a tool can save time in interpreting chest X-rays and increase the accuracy and thereby enhance our medical capacity for the detection and diagnosis of COVID-19. The explainable method is also used in the DCNN to select instances of the X-ray dataset images to explain the behavior of training-learning models to achieve higher prediction accuracy. The average accuracy of our method is above 96%, which can replace manual reading and has the potential to be applied to large-scale rapid screening of COVID-9 for widely use cases.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia
/
Algorithms
/
Neural Networks, Computer
/
Deep Learning
/
COVID-19
Type of study:
Diagnostic study
/
Observational study
/
Prognostic study
Topics:
Long Covid
Limits:
Humans
Language:
English
Journal:
Sci Rep
Year:
2021
Document Type:
Article
Affiliation country:
S41598-021-95680-6
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