Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection Article Swipe
Jimi Togni
,
Romis Attux
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.14209/sbrt.2021.157072730
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.14209/sbrt.2021.157072730
This study presents a comparative analysis of methods for detecting COVID-19 infection in radiographic images. The images, sourced from publicly available datasets, were categorized into three classes: 'normal,' 'pneumonia,' and 'COVID.' For the experiments, transfer learning was employed using eight pre-trained networks: SqueezeNet, DenseNet, ResNet, AlexNet, VGG, GoogleNet, ShuffleNet, and MobileNet. DenseNet achieved the highest accuracy of 97.64% using the ADAM optimization function in the multiclass approach. In the binary classification approach, the highest precision was 99.98%, obtained by the VGG, ResNet, and MobileNet networks. A comparative evaluation was also conducted using heat maps.
Related Topics
Concepts
Coronavirus disease 2019 (COVID-19)
Image (mathematics)
Artificial intelligence
Computer science
2019-20 coronavirus outbreak
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Pattern recognition (psychology)
Computer vision
Virology
Medicine
Pathology
Infectious disease (medical specialty)
Disease
Outbreak
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2501.04196
- https://arxiv.org/pdf/2501.04196
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406231139
All OpenAlex metadata
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https://openalex.org/W4406231139Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.14209/sbrt.2021.157072730Digital Object Identifier
- Title
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Comparison of Neural Models for X-ray Image Classification in COVID-19 DetectionWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-08Full publication date if available
- Authors
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Jimi Togni, Romis AttuxList of authors in order
- Landing page
-
https://arxiv.org/abs/2501.04196Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2501.04196Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2501.04196Direct OA link when available
- Concepts
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Coronavirus disease 2019 (COVID-19), Image (mathematics), Artificial intelligence, Computer science, 2019-20 coronavirus outbreak, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Pattern recognition (psychology), Computer vision, Virology, Medicine, Pathology, Infectious disease (medical specialty), Disease, OutbreakTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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