Classifying chest CT images as COVID-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method.
BMC Bioinformatics
; 22(Suppl 5): 147, 2021 Nov 08.
Article
in English
| MEDLINE | ID: covidwho-1505775
ABSTRACT
BACKGROUND:
To classify chest computed tomography (CT) images as positive or negative for coronavirus disease 2019 (COVID-19) quickly and accurately, researchers attempted to develop effective models by using medical images.RESULTS:
A convolutional neural network (CNN) ensemble model was developed for classifying chest CT images as positive or negative for COVID-19. To classify chest CT images acquired from COVID-19 patients, the proposed COVID19-CNN ensemble model combines the use of multiple trained CNN models with a majority voting strategy. The CNN models were trained to classify chest CT images by transfer learning from well-known pre-trained CNN models and by applying their algorithm hyperparameters as appropriate. The combination of algorithm hyperparameters for a pre-trained CNN model was determined by uniform experimental design. The chest CT images (405 from COVID-19 patients and 397 from healthy patients) used for training and performance testing of the COVID19-CNN ensemble model were obtained from an earlier study by Hu in 2020. Experiments showed that, the COVID19-CNN ensemble model achieved 96.7% accuracy in classifying CT images as COVID-19 positive or negative, which was superior to the accuracies obtained by the individual trained CNN models. Other performance measures (i.e., precision, recall, specificity, and F1-score) obtained bythe COVID19-CNN ensemble model were higher than those obtained by individual trained CNN models.CONCLUSIONS:
The COVID19-CNN ensemble model had superior accuracy and excellent capability in classifying chest CT images as COVID-19 positive or negative.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Deep Learning
/
COVID-19
Type of study:
Experimental Studies
/
Prognostic study
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal:
BMC Bioinformatics
Journal subject:
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
S12859-021-04083-x
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