Transfer Learning Model in Deep Neural Network for COVID-19 Prediction from CT Images
4th International Conference on Electrical, Computer and Telecommunication Engineering, ICECTE 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-20237209
ABSTRACT
Deep learning models are often used to process radi-ological images automatically and can accurately train networks' weights on appropriate datasets. One of the significant benefits of the network is that it is possible to use the weight of a pre-trained network for other applications by fine-tuning the current weight. The primary purpose of this work is to employ a pre-trained deep neural framework known as transfer learning to detect and diagnose COVID-19 in CT images automatically. This paper uses a popular deep neural model, ResNet152, as a neural transfer approach. The presented framework uses the weight obtained from the ImageNet dataset, fine-tuned by the dataset used in the work. The effectiveness of the suggested COVID-19 prediction system is evaluated experimentally and compared with DenseNet, another transfer learning model. The recommended ResNet152 transfer learning model exhibits improved performance and has a 99% accuracy when analogized with the DenseNet201 transfer learning model. © 2022 IEEE.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
4th International Conference on Electrical, Computer and Telecommunication Engineering, ICECTE 2022
Year:
2022
Document Type:
Article
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