COVID-19 diagnosis on CT scan images using a generative adversarial network and concatenated feature pyramid network with an attention mechanism.
Med Phys
; 48(8): 4334-4349, 2021 Aug.
Article
in English
| MEDLINE | ID: covidwho-1265402
ABSTRACT
OBJECTIVE:
Coronavirus disease 2019 (COVID-19) has caused hundreds of thousands of infections and deaths. Efficient diagnostic methods could help curb its global spread. The purpose of this study was to develop and evaluate a method for accurately diagnosing COVID-19 based on computed tomography (CT) scans in real time.METHODS:
We propose an architecture named "concatenated feature pyramid network" ("Concat-FPN") with an attention mechanism, by concatenating feature maps of multiple. The proposed architecture is then used to form two networks, which we call COVID-CT-GAN and COVID-CT-DenseNet, the former for data augmentation and the latter for data classification.RESULTS:
The proposed method is evaluated on 3 different numbers of magnitude of COVID-19 CT datasets. Compared with the method without GANs for data augmentation or the original network auxiliary classifier generative adversarial network, COVID-CT-GAN increases the accuracy by 2% to 3%, the recall by 2% to 4%, the precision by 1% to 3%, the F1-score by 1% to 3%, and the area under the curve by 1% to 4%. Compared with the original network DenseNet-201, COVID-CT-DenseNet increases the accuracy by 1% to 3%, the recall by 4% to 9%, the precision by 1%, the F1-score by 1% to 3%, and the area under the curve by 2%.CONCLUSION:
The experimental results show that our method improves the efficiency of diagnosing COVID-19 on CT images, and helps overcome the problem of limited training data when using deep learning methods to diagnose COVID-19.SIGNIFICANCE:
Our method can help clinicians build deep learning models using their private datasets to achieve automatic diagnosis of COVID-19 with a high precision.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Med Phys
Year:
2021
Document Type:
Article
Affiliation country:
Mp.15044
Similar
MEDLINE
...
LILACS
LIS