COVID-19: X-ray image classification using transfer learning DCNN approach
3rd International Conference on Innovations in Communication Computing and Sciences, ICCS 2021
; 2576, 2022.
Article
in English
| Scopus | ID: covidwho-2186579
ABSTRACT
COVID-19 is a coronavirus that causes sickness in the human respiratory system. It is the most recent virus that is wreaking havoc on the entire world. It spreads mainly through contact with an infected person. There are some vaccinations available to prevent this condition now. The flu causes symptoms such as fever, coughing, and breathing difficulties in humans. COVID-19 Classification of X-Ray Images This paper suggests using a Deep Convolution Neural Network-based Transfer Learning methodology. Deep CNN learns picture patterns and classifies X-RAY pictures using transfer learning technology. A dataset is created using publicly available photos of COVID-19 X-Ray. All images have been resized and rotated by 2 to 20 degrees. The file contains 6677 COVID-19 pictures and 5753 stock pictures. DCNN predictability is 99.64 percent on a training set, while on a test set, it is 99.79 percent. After the transfer of learning, predictive accuracy on the training set is 99.19 percent, while predictive accuracy on the test set is 99.31 percent. © 2022 Author(s).
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
3rd International Conference on Innovations in Communication Computing and Sciences, ICCS 2021
Year:
2022
Document Type:
Article
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