2020
DOI: 10.1007/s13246-020-00934-8
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Issues associated with deploying CNN transfer learning to detect COVID-19 from chest X-rays

Abstract: Covid-19 first occurred in Wuhan, China in December 2019. Subsequently, the virus spread throughout the world and as of June 2020 the total number of confirmed cases are above 4.7 million with over 315,000 deaths. Machine learning algorithms built on radiography images can be used as a decision support mechanism to aid radiologists to speed up the diagnostic process. The aim of this work is to conduct a critical analysis to investigate the applicability of convolutional neural networks (CNNs) for the purpose o… Show more

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Cited by 38 publications
(24 citation statements)
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References 34 publications
(37 reference statements)
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“…On two datasets, this method has 96% and 97% test accuracy, respectively. These performances are superior in comparison to other works like 89.60% test accuracy achieved by Khan et al [28], 95% test accuracy achieved by Waheed [29] and 87.02% test accuracy achieved by Majeed et al [2].…”
Section: Resultscontrasting
confidence: 58%
See 1 more Smart Citation
“…On two datasets, this method has 96% and 97% test accuracy, respectively. These performances are superior in comparison to other works like 89.60% test accuracy achieved by Khan et al [28], 95% test accuracy achieved by Waheed [29] and 87.02% test accuracy achieved by Majeed et al [2].…”
Section: Resultscontrasting
confidence: 58%
“…Majeed et al [2] have provided an analysis of 12 regular convolutional neural network models to help radiologists discriminate against COVID-19 based on chest X-rays, and also have introduced a CNN model that could give efficient final prediction results. The proposed model has been designed to perform reliable diagnostics for COVID vs. Normal classification and COVID vs. Normal vs.…”
Section: Related Workmentioning
confidence: 99%
“…DeTraC used class decomposition mechanism to deal with class boundaries leading to an accuracy of 0.931 and sensitivity of 1. A work by Taban et al on chest X-ray images used the 12-off-the-shelf CNN architectures in transfer learning [ 16 ]. GANs have also been used in the process of COVID-19 disease analysis by Mohamed et al [ 17 ].…”
Section: Introductionmentioning
confidence: 99%
“…The CNN architecture proposed in the study was found to outperform 7 out of 12 established CNN architectures: AlexNet, GoogleNet, Vgg16, Vgg19, ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetv2, SqueezeNet, Densenet201 and Xception. In another study, Ouyang et al [35] proposed an online attention module with 3D CNN to diagnose COVID-19 from CT scan images, and an accuracy of 87.5% was achieved.…”
Section: Related Workmentioning
confidence: 99%