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. 2022 Aug;40(13):5836-5847.
doi: 10.1080/07391102.2021.1875049. Epub 2021 Jan 21.

ADOPT: automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images

Gaurav Dhiman  1 , Victor Chang  2 , Krishna Kant Singh  3 , Achyut Shankar  4
Affiliations

ADOPT: automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images

Gaurav Dhiman et al. J Biomol Struct Dyn. 2022 Aug.

Abstract

In the hospital, because of the rise in cases daily, there are a small number of COVID-19 test kits available. For this purpose, a rapid alternative diagnostic choice to prevent COVID-19 spread among individuals must be implemented as an automatic detection method. In this article, the multi-objective optimization and deep learning-based technique for identifying infected patients with coronavirus using X-rays is proposed. J48 decision tree approach classifies the deep feature of corona affected X-ray images for the efficient detection of infected patients. In this study, 11 different convolutional neural network-based (CNN) models (AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201 and XceptionNet) are developed for detection of infected patients with coronavirus pneumonia using X-ray images. The efficiency of the proposed model is tested using k-fold cross-validation method. Moreover, the parameters of CNN deep learning model are tuned using multi-objective spotted hyena optimizer (MOSHO). Extensive analysis shows that the proposed model can classify the X-ray images at a good accuracy, precision, recall, specificity and F1-score rates. Extensive experimental results reveal that the proposed model outperforms competitive models in terms of well-known performance metrics. Hence, the proposed model is useful for real-time COVID-19 disease classification from X-ray chest images.Communicated by Ramaswamy H. Sarma.

Keywords: CNN; COVID-19; Coronavirus; J48; MOSHO; deep learning; optimization.

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Conflict of interest statement

No potential conflict of interest was reported by the authors.

Figures

Figure 1.
Figure 1.
Epidemic curve of confirmed COVID-19 provided by WHO.
Figure 2.
Figure 2.
COVID-19 classification approach.
Figure 3.
Figure 3.
Neural network.
Figure 4.
Figure 4.
Architecture of CNN.
Figure 5.
Figure 5.
X-ray images of coronavirus (COVID-19) disease effected patients.
Figure 6.
Figure 6.
X-ray images of normal patients.
Figure 7.
Figure 7.
The accuracy results using different classification models for k = 5.
Figure 8.
Figure 8.
The recall results using different classification models for k = 5.
Figure 9.
Figure 9.
The specificity results using different classification models for k = 5.
Figure 10.
Figure 10.
The precision results using different classification models for k = 5.
Figure 11.
Figure 11.
The F1-Score results using different classification models for k = 5.
Figure 12.
Figure 12.
The accuracy results using different classification models for k = 10.
Figure 13.
Figure 13.
The recall results using different classification models for k = 10.
Figure 14.
Figure 14.
The specificity results using different classification models for k = 10.
Figure 15.
Figure 15.
The precision results using different classification models for k = 10.
Figure 16.
Figure 16.
The F1-score results using different classification models for k = 10.
Figure 17.
Figure 17.
Segmented chest area of normal patients using CNN approach.
Figure 18.
Figure 18.
Segmented chest area of COVID-19 patients using CNN approach.
Figure 19.
Figure 19.
Calculated computational time to predict the COVID-19 disease using different CNN models.
See this image and copyright information in PMC

References

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