A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 Model Article Swipe
Ali Alqahtani
,
Shumaila Akram
,
Muhammad Ramzan
,
Fouzia Nawaz
,
Hikmat Ullah Khan
,
Essa Alhashlan
,
Samar M. Alqhtani
,
Areeba Waris
,
Zain Ali
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32604/iasc.2023.025597
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.32604/iasc.2023.025597
Coronavirus (COVID-19 or SARS-CoV-2) is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries. The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission. There is a significant increase in the number of patients infected, resulting in a lack of test resources and kits in most countries. To overcome this panicked state of affairs, researchers are looking forward to some effective solutions to overcome this situation: one of the most common and effective methods is to examine the X-radiation (X-rays) and computed tomography (CT) images for detection of Covid-19. However, this method burdens the radiologist to examine each report. Therefore, to reduce the burden on the radiologist, an effective, robust and reliable detection system has been developed, which may assist the radiologist and medical specialist in effective detecting of COVID. We proposed a deep learning approach that uses readily available chest radio-graphs (chest X-rays) to diagnose COVID-19 cases. The proposed approach applied transfer learning to the Deep Convolutional Neural Network (DCNN) model, Inception-v4, for the automatic detection of COVID-19 infection from chest X-rays images. The dataset used in this study contains 1504 chest X-ray images, 504 images of COVID-19 infection, and 1000 normal images obtained from publicly available medical repositories. The results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%.
Related Topics
Concepts
Coronavirus disease 2019 (COVID-19)
Computer science
Transfer of learning
Convolutional neural network
Deep learning
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Artificial intelligence
Transmission (telecommunications)
Medicine
Telecommunications
Pathology
Disease
Infectious disease (medical specialty)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/iasc.2023.025597
- https://file.techscience.com/ueditor/files/iasc/TSP_IASC-35-2/TSP_IASC_25597/TSP_IASC_25597.pdf
- OA Status
- hybrid
- Cited By
- 7
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285791437
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285791437Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.32604/iasc.2023.025597Digital Object Identifier
- Title
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A Transfer Learning Based Approach for COVID-19 Detection Using Inception-v4 ModelWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-19Full publication date if available
- Authors
-
Ali Alqahtani, Shumaila Akram, Muhammad Ramzan, Fouzia Nawaz, Hikmat Ullah Khan, Essa Alhashlan, Samar M. Alqhtani, Areeba Waris, Zain AliList of authors in order
- Landing page
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https://doi.org/10.32604/iasc.2023.025597Publisher landing page
- PDF URL
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https://file.techscience.com/ueditor/files/iasc/TSP_IASC-35-2/TSP_IASC_25597/TSP_IASC_25597.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://file.techscience.com/ueditor/files/iasc/TSP_IASC-35-2/TSP_IASC_25597/TSP_IASC_25597.pdfDirect OA link when available
- Concepts
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Coronavirus disease 2019 (COVID-19), Computer science, Transfer of learning, Convolutional neural network, Deep learning, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Artificial intelligence, Transmission (telecommunications), Medicine, Telecommunications, Pathology, Disease, Infectious disease (medical specialty)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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47Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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