Transfer Learning for Detecting Covid-19 Cases Using Chest X-Ray Images

International Journal of Machine Learning and Networked Collaborative Engineering

View Publication Info
 
 
Field Value
 
Title Transfer Learning for Detecting Covid-19 Cases Using Chest X-Ray Images
 
Creator Alotaibi, Abdulmohsen
 
Subject Machine learning
Transfer learning
COVID-19
Coronavirus
ImageNet
 
Description The COVID-19 pandemic is a global health crisis that have already infected more than 3.5 million people and caused more than 250 thousand deaths around the globe. That is why it is critical to develop a more efficient way to detect and treat this illness. This paper utilizes transfer learning techniques to detect normal, COVID-19, and viral pneumonia cases from Chest X-Ray images. Four pre-trained models on ImageNet were chosen as the base model, which are ResNet50, VGG19, DenseNet121, and InceptionV3. The performance metrics of each fine-tuned model are overall similar. With an average recall, precision, f1-score, and accuracy of 97.42%, 97.42%, 97.23%, 98.3% respectively.
 
Publisher SR Informatics, New Delhi, India
 
Date 2020-08-17
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://www.mlnce.net/index.php/Home/article/view/136
 
Source International Journal of Machine Learning and Networked Collaborative Engineering; Vol. 4 No. 01 (2020): Volume No 04 Issue No 01; 21-29
2581-3242
 
Language eng
 
Relation http://www.mlnce.net/index.php/Home/article/view/136/76
 
Rights Copyright (c) 2020 International Journal of Machine Learning and Networked Collaborative Engineering
http://creativecommons.org/licenses/by-nc-nd/4.0
 

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