Pre-trained deep learning models in automatic COVID-19 diagnosis
Coronavirus Disease (COVID-19) is a devastating pandemic in the history of mankind. It is a highly contagious flu that can spread from human to human without revealing any symptoms. For being so contagious, detecting patients with it and isolating them has become the primary concern for healthcare p...
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| Main Authors: | , , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2021-06-01.
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| Online Access: | Get fulltext |
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| LEADER | 02340 am a22003253u 4500 | ||
|---|---|---|---|
| 001 | ijeecs24225_15085 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Reza, Ahmed Wasif |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Hasan, Md Mahamudul |e author |
| 700 | 1 | 0 | |a Nowrin, Nazla |e author |
| 700 | 1 | 0 | |a Ahmed Shibly, Mir Moynuddin |e author |
| 245 | 0 | 0 | |a Pre-trained deep learning models in automatic COVID-19 diagnosis |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-06-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24225 | ||
| 520 | |a Coronavirus Disease (COVID-19) is a devastating pandemic in the history of mankind. It is a highly contagious flu that can spread from human to human without revealing any symptoms. For being so contagious, detecting patients with it and isolating them has become the primary concern for healthcare professionals. This study presented an alternative way to identify COVID-19 patients by doing an automatic examination of chest X-rays of the patients. To develop such an efficient system, six pre-trained deep learning models were used. Those models were: VGG16, InceptionV3, Xception, DenseNet201, InceptionResNetV2, and EfficientNetB4. Those models were developed on two open-source datasets that have chest X-rays of patients diagnosed with COVID-19. Among the models, EfficientNetB4 achieved better performances on both datasets with 96% and 97% of accuracies. The empirical results were also exemplary. This type of automated system can help us fight this dangerous virus outbreak. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a biomedical image classification; COVID-19; deep learning; efficientNetB4; transfer learning; | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 22, No 3: June 2021; 1540-1547 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v22.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24225/15085 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/24225/15085 |z Get fulltext |