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. 2021 Nov 8;22(Suppl 5):147.
doi: 10.1186/s12859-021-04083-x.

Classifying chest CT images as COVID-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method

Yao-Mei Chen  1   2 , Yenming J Chen  3 , Wen-Hsien Ho  4   5 , Jinn-Tsong Tsai  6   7
Affiliations

Classifying chest CT images as COVID-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method

Yao-Mei Chen et al. BMC Bioinformatics. .

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: Algorithm hyperparameter; COVID-19; Chest computed tomography image; Convolutional neural network; Ensemble model.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Representative CT images in the COVID-19 and Normal classes
Fig. 2
Fig. 2
Progressive improvement in accuracy of VGG-19#6
Fig. 3
Fig. 3
Progressive improvement in accuracy of Resnet-101#7
Fig. 4
Fig. 4
Progressive improvement in accuracy of DenseNet-201#7
Fig. 5
Fig. 5
Progressive improvement in accuracy of Inception-v3#7
Fig. 6
Fig. 6
Progressive improvement in accuracy of Inception-ResNet-v2#7
Fig. 7
Fig. 7
Flowchart of transfer learning procedure used in the CNN model
See this image and copyright information in PMC

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