The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization
Open accessAbstract
In this paper, a novel approach called GSA-DenseNet121-COVID-19 based on a hybrid convolutional neural network (CNN) architecture is proposed using an optimization algorithm. The CNN architecture that was used is called DenseNet121, and the optimization algorithm that was used is called the gravitational search algorithm (GSA). The GSA is used to determine the best values for the hyperparameters of the DenseNet121 architecture. To help this architecture to achieve a high level of accuracy in diagnosing COVID-19 through chest x-ray images. The obtained results showed that the proposed approach could classify 98.38% of the test set correctly. To test the efficacy of the GSA in setting the optimum values for the hyperparameters of DenseNet121. The GSA was compared to another approach called SSD-DenseNet121, which depends on the DenseNet121 and the optimization algorithm called social ski driver (SSD). The comparison results demonstrated the efficacy of the proposed GSA-DenseNet121-COVID-19. As it was able to diagnose COVID-19 better than SSD-DenseNet121 as the second was able to diagnose only 94% of the test set. The proposed approach was also compared to another method based on a CNN architecture called Inception-v3 and manual search to quantify hyperparameter values. The comparison results showed that the GSA-DenseNet121-COVID-19 was able to beat the comparison method, as the second was able to classify only 95% of the test set samples. The proposed GSA-DenseNet121-COVID-19 was also compared with some related work. The comparison results showed that GSA-DenseNet121-COVID-19 is very competitive.
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Cited by
Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00307-0 · Cited by 1,044
Roberts, M. T., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Avilés-Rivero, A. I., Etmann, C., McCague, C., Beer, L., et al. (2020) · Apollo (University of Cambridge) · doi:10.17863/cam.64000 · Cited by 895
A machine learning forecasting model for COVID-19 pandemic in India
Sujath, R., Chatterjee, J. M., & Hassanien, A. E. (2020) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-020-01827-8 · Cited by 339
Self-citationA Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)
Islam, M. M., Karray, F., Alhajj, R., & Zeng, J. (2021) · IEEE Access · doi:10.1109/access.2021.3058537 · Cited by 292
Applications of artificial intelligence in battling against covid-19: A literature review
Tayarani N., M.-H. (2021) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110338 · Cited by 210
Metadata
- Authors
- Ezzat, D., Hassanien, A. E., & Ella, H. A.
- Venue
- Applied Soft Computing
- Publisher
- Elsevier BV
- Published
- 2021-01
- Volume
- 98
- Pages
- 106742
- ISSN
- 1568-4946 (ISSN-L)
- OpenAlex
- W3015538848
- Cited by
- 217
- References
- 11
- FWCI
- 17.82
- Type
- journal article
- Language
- en
- Open access
- green
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