Deep Learning-Aided Automated Pneumonia Detection and Classification Using CXR Scans Article Swipe
Deepak Kumar Jain
,
Tarishi Singh
,
Praneet Saurabh
,
Dhananjay Bisen
,
Neeraj Sahu
,
Jayant Kumar Mishra
,
Md Habibur Rahman
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/7474304
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/7474304
The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled almost all countries. The pandemic has mounted pressure on the existing healthcare system and caused panic and desperation. The gold testing standard for COVID-19 detection, reverse transcription-polymerase chain reaction (RT-PCR), has shown its limitations with 70% accuracy, contributing to the incorrect diagnosis that exaggerated the complexities and increased the fatalities. The new variations further pose unseen challenges in terms of their diagnosis and subsequent treatment. The COVID-19 virus heavily impacts the lungs and fills the air sacs with fluid causing pneumonia. Thus, chest X-ray inspection is a viable option if the inspection detects COVID-19-induced pneumonia, hence confirming the exposure of COVID-19. Artificial intelligence and machine learning techniques are capable of examining chest X-rays in order to detect patterns that can confirm the presence of COVID-19-induced pneumonia. This research used CNN and deep learning techniques to detect COVID-19-induced pneumonia from chest X-rays. Transfer learning with fine-tuning ensures that the proposed work successfully classifies COVID-19-induced pneumonia, regular pneumonia, and normal conditions. Xception, Visual Geometry Group 16, and Visual Geometry Group 19 are used to realize transfer learning. The experimental results were promising in terms of precision, recall, F1 score, specificity, false omission rate, false negative rate, false positive rate, and false discovery rate with a COVID-19-induced pneumonia detection accuracy of 98%. Experimental results also revealed that the proposed work has not only correctly identified COVID-19 exposure but also made a distinction between COVID-19-induced pneumonia and regular pneumonia, as the latter is a very common disease, while COVID-19 is more lethal. These results mitigated the concern and overlap in the diagnosis of COVID-19-induced pneumonia and regular pneumonia. With further integrations, it can be employed as a potential standard model in differentiating the various lung-related infections, including COVID-19.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/7474304
- https://downloads.hindawi.com/journals/cin/2022/7474304.pdf
- OA Status
- hybrid
- Cited By
- 21
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4289856205
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4289856205Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2022/7474304Digital Object Identifier
- Title
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Deep Learning-Aided Automated Pneumonia Detection and Classification Using CXR ScansWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-08-04Full publication date if available
- Authors
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Deepak Kumar Jain, Tarishi Singh, Praneet Saurabh, Dhananjay Bisen, Neeraj Sahu, Jayant Kumar Mishra, Md Habibur RahmanList of authors in order
- Landing page
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https://doi.org/10.1155/2022/7474304Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/cin/2022/7474304.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
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https://downloads.hindawi.com/journals/cin/2022/7474304.pdfDirect OA link when available
- Concepts
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Pneumonia, Coronavirus disease 2019 (COVID-19), Artificial intelligence, Transfer of learning, False positive rate, Computer science, Gold standard (test), Machine learning, Medicine, Radiology, Pathology, Internal medicine, Infectious disease (medical specialty), DiseaseTop concepts (fields/topics) attached by OpenAlex
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21Total citation count in OpenAlex
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2025: 5, 2024: 11, 2023: 4, 2022: 1Per-year citation counts (last 5 years)
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29Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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