Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 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.

PubMed Disclaimer

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

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, X., Sun, Z., & Xia, L. (2020). Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases. Radiology, 296(2), E32–E23. 10.1148/radiol.2020200642 - DOI - PMC - PubMed
    1. Celik, Y., Talo, M., Yildirim, O., Karabatak, M., & Acharya, U. R. (2020). Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images. Pattern Recognition Letters, 133, 232–239. 10.1016/j.patrec.2020.03.011 - DOI
    1. Chan, J. F., Yuan, S., & Kok, K. H. (2020). A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster. Lancet, 395(10223), P514–P523.10.1016/S0140-6736(20)30154-9. - PMC - PubMed
    1. Dhiman, G. (2019). ESA: A hybrid bio-inspired metaheuristic optimization approach for engineering problems. Engineering with Computers, 1–31.
    1. Dhiman, G. (2020). MOSHEPO: A hybrid multi-objective approach to solve economic load dispatch and micro grid problems. Applied Intelligence, 50(1), 119–137. 10.1007/s10489-019-01522-4 - DOI

LinkOut - more resources