Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images.
IEEE/ACM Trans Comput Biol Bioinform
; 18(6): 2775-2780, 2021.
Article
in English
| MEDLINE | ID: covidwho-1559565
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Here, we have collected chest CT scans of 88 patients diagnosed with COVID-19 from hospitals of two provinces in China, 100 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the data, a deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model could accurately discriminate the COVID-19 patients from the bacteria pneumonia patients with an AUC of 0.95, recall (sensitivity) of 0.96, and precision of 0.79. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO), which are visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by our server (http//biomed.nscc-gz.cn/model.php). Source codes and datasets are available at our GitHub (https//github.com/SY575/COVID19-CT).
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Tomography, X-Ray Computed
/
Diagnosis, Computer-Assisted
/
Deep Learning
/
COVID-19
Type of study:
Diagnostic study
/
Observational study
Limits:
Humans
Country/Region as subject:
Asia
Language:
English
Journal:
ACM Trans Comput Biol Bioinform
Journal subject:
Biology
/
Medical Informatics
Year:
2021
Document Type:
Article
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