Transfer learning with Resnet-50 for detecting COVID-19 in chest X-ray images
The novel coronavirus, also known as COVID-19, initially appeared in Wuhan, China, in December 2019 and has since spread around the world. The purpose of this paper is to use deep convolutional neural networks (DCCN) to improve the detection of COVID-19 from X-ray images. In this study, we create a...
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| Main Authors: | , , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2022-03-01.
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| Online Access: | Get fulltext |
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| LEADER | 02482 am a22003253u 4500 | ||
|---|---|---|---|
| 001 | ijeecs27057_16119 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Hamlili, Fatima-Zohra |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Beladgham, Mohammed |e author |
| 700 | 1 | 0 | |a Khelifi, Mustapha |e author |
| 700 | 1 | 0 | |a Bouida, Ahmed |e author |
| 245 | 0 | 0 | |a Transfer learning with Resnet-50 for detecting COVID-19 in chest X-ray images |
| 260 | |b Institute of Advanced Engineering and Science, |c 2022-03-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/27057 | ||
| 520 | |a The novel coronavirus, also known as COVID-19, initially appeared in Wuhan, China, in December 2019 and has since spread around the world. The purpose of this paper is to use deep convolutional neural networks (DCCN) to improve the detection of COVID-19 from X-ray images. In this study, we create a DCNN based on a residual network (Resnet-50) that can identify COVID-19 from two other classes (pneumonia and normal) in chest X-ray images. DCNN was evaluated using two classification methods: binary (BC-1: COVID-19 vs. normal, BC-2: COVID-19 vs. pneumonia) and multi-class (pneumonia vs. normal vs. COVID-19). In all experiments, four fold cross-validation was used to train and test the model. This architecture's average accuracy is 99.9% for BC-1, 99.8% for BC-2, and 97.3% for multi-class cases. The experimental findings demonstrated that the suggested system detects COVID-19 with an average precision and sensitivity of 95% and 95.1% for multi-class classification, respectively. According to our findings, the proposed DCNN may help health professionals in confirming their first evaluation of COVID-19 patients. | ||
| 540 | |a Copyright (c) 2022 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Chest X-ray images; Classification; COVID-19; Deep-learning; Pre-trained deep CNN model | ||
| 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 25, No 3: March 2022; 1458-1468 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v25.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/27057/16119 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/27057/16119 |z Get fulltext |