Fast automated detection of COVID-19 from medical images using convolutional neural networks.
Commun Biol
; 4(1): 35, 2021 01 04.
Article
in English
| MEDLINE | ID: covidwho-1065967
ABSTRACT
Coronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity. We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography images to train the convolutional neural network, which achieves a performance similar to that of experts and provides high scores for multiple statistical indices (F1 scores > 96.72% (0.9307, 0.9890) and specificity >99.33% (0.9792, 1.0000)). Heatmaps are used to visualize the salient features extracted by the neural network. The neural network-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Radiographic Image Interpretation, Computer-Assisted
/
Tomography, X-Ray Computed
/
Neural Networks, Computer
/
Deep Learning
/
SARS-CoV-2
/
COVID-19
Type of study:
Diagnostic study
/
Observational study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Commun Biol
Year:
2021
Document Type:
Article
Affiliation country:
S42003-020-01535-7
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