Classification of COVID-19 and lung opacity using vision transformer on chest x-ray images Article Swipe
Manoochehr Noghanian Toroghi
,
Usman Ullah Sheikh
,
Shima Shahi Irani
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2622/1/012016
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2622/1/012016
There are several recent works which had proposed an automatic computer-aided diagnosis (CAD) deep learning (DL) model to diagnose coronavirus disease 2019 (COVID-19) using chest x-ray images (CXR) to propose a high-accuracy CAD method to detect COVID-19 automatically. In this study, seven different models including Convolutional Neural Network (CNN) models such as VGG-16 and vision transformer (ViT) models, are proposed. The different proposed models are trained with a three-class balanced dataset consisting of 3,000 CXR images consisting of 1,000 CXR images for each class of COVID-19, Normal, and Lung-Opacity. A publicly available dataset to train and test the models is used from Kaggle-COVID-19-Radiography-Dataset. From the experiments, the accuracy of the VGG16 model is 93.44% and ViT’s is 92.33%. Besides, the binary classification between two classes of COVID-19 and Normal CXR with a limited number of just 100 images for each class, using a transfer learning technique, with a validation accuracy of 97.5% is proposed.
Related Topics
Concepts
Convolutional neural network
Coronavirus disease 2019 (COVID-19)
Artificial intelligence
Computer science
Transfer of learning
Pattern recognition (psychology)
Deep learning
Radiography
CAD
Transformer
Opacity
Computer-aided diagnosis
Binary classification
Medicine
Radiology
Support vector machine
Pathology
Infectious disease (medical specialty)
Disease
Optics
Physics
Voltage
Quantum mechanics
Engineering drawing
Engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2622/1/012016
- https://iopscience.iop.org/article/10.1088/1742-6596/2622/1/012016/pdf
- OA Status
- diamond
- Cited By
- 2
- References
- 9
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388698819
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388698819Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1742-6596/2622/1/012016Digital Object Identifier
- Title
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Classification of COVID-19 and lung opacity using vision transformer on chest x-ray imagesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-10-01Full publication date if available
- Authors
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Manoochehr Noghanian Toroghi, Usman Ullah Sheikh, Shima Shahi IraniList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/2622/1/012016Publisher landing page
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https://iopscience.iop.org/article/10.1088/1742-6596/2622/1/012016/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://iopscience.iop.org/article/10.1088/1742-6596/2622/1/012016/pdfDirect OA link when available
- Concepts
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Convolutional neural network, Coronavirus disease 2019 (COVID-19), Artificial intelligence, Computer science, Transfer of learning, Pattern recognition (psychology), Deep learning, Radiography, CAD, Transformer, Opacity, Computer-aided diagnosis, Binary classification, Medicine, Radiology, Support vector machine, Pathology, Infectious disease (medical specialty), Disease, Optics, Physics, Voltage, Quantum mechanics, Engineering drawing, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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9Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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