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. 2021 Oct:110:107645.
doi: 10.1016/j.asoc.2021.107645. Epub 2021 Jun 25.

Coronavirus disease (COVID-19) detection using X-ray images and enhanced DenseNet

Saleh Albahli  1 , Nasir Ayub  2 , Muhammad Shiraz  2
Affiliations

Coronavirus disease (COVID-19) detection using X-ray images and enhanced DenseNet

Saleh Albahli et al. Appl Soft Comput. 2021 Oct.

Abstract

The 2019 novel coronavirus (COVID-19) originating from China, has spread rapidly among people living in other countries. According to the World Health Organization (WHO), by the end of January, more than 104 million people have been affected by COVID-19, including more than 2 million deaths. The number of COVID-19 test kits available in hospitals is reduced due to the increase in regular cases. Therefore, an automatic detection system should be introduced as a fast, alternative diagnostic to prevent COVID-19 from spreading among humans. For this purpose, three different BiT models: DenseNet, InceptionV3, and Inception-ResNetV4 have been proposed in this analysis for the diagnosis of patients infected with coronavirus pneumonia using X-ray radiographs in the chest. These three models give and examine Receiver Operating Characteristic (ROC) analyses and uncertainty matrices, using 5-fold cross-validation. We have performed the simulations which have visualized that the pre-trained DenseNet model has the best classification efficiency with 92% among two other models proposed (83.47% accuracy for inception V3 and 85.57% accuracy for Inception-ResNetV4).

Keywords: Biomedical imaging; COVID-19; Convolutional neural network; Deep learning; DenseNet; ResNet.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Normal chest X-ray.
Fig. 2
Fig. 2
Covid-19 affected chest X-ray.
Fig. 3
Fig. 3
Training loss curve when samplewise_center augmentation is applied (without model learning).
Fig. 4
Fig. 4
Training loss curve when samplewise_std_normalization augmentation is applied (with model learning).
Fig. 5
Fig. 5
Contribution before introducing weights for each class.
Fig. 6
Fig. 6
Contribution after introducing weights for each class.
Fig. 7
Fig. 7
Labeling after choosing the right value of parameter c.
Fig. 8
Fig. 8
Different classes after optimal value of parameter c.
Fig. 9
Fig. 9
Residual Layer: Building block of ResNet .
Fig. 10
Fig. 10
Architecture of different ResNet models .
Fig. 11
Fig. 11
Training loss vs. Epoch.
Fig. 12
Fig. 12
Basic architecture of DenseNet models .
Fig. 13
Fig. 13
Inception-V3 architecture.
Fig. 14
Fig. 14
R101 × 1 TPR vs. FPR.
Fig. 15
Fig. 15
DenseNet TPR vs. FPR.
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