Comparison of different optimizers implemented on the deep learning architectures for COVID-19 classification.
Mater Today Proc
; 46: 11098-11102, 2021.
Article
in English
| MEDLINE | ID: covidwho-1096154
ABSTRACT
COVID-19 is the present-day pandemic around the globe. WHO has estimated that approx 15% of the world's population may have been infected with coronavirus with a large number of population on the verge of being infected. It is quite difficult to break the virus chain since asymptomatic patients can result in the spreading of the infection apart from the seriously infected patients. COVID-19 has many similar symptoms to SARS-D however, the symptoms can worsen depending on the immunity power of the patients. It is necessary to be able to find the infected patients even with no symptoms to be able to break the spread of the chain. In this paper, the comparison table describes the accuracy of deep learning architectures by the implementation of different optimizers with different learning rates. In order to remove the overfitting issue, different learning rate has been experimented. Further in this paper, we have proposed the classification of the COVID-19 images using the ensemble of 2 layered Convolutional Neural Network with the Transfer learning method which consumed lesser time for classification and attained an accuracy of nearly 90.45%.
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Collection:
International databases
Database:
MEDLINE
Language:
English
Journal:
Mater Today Proc
Year:
2021
Document Type:
Article
Affiliation country:
J.matpr.2021.02.244
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