A bi-stage feature selection approach for COVID-19 prediction using chest CT images Article Swipe
Shibaprasad Sen
,
Soumyajit Saha
,
Somnath Chatterjee
,
Seyedali Mirjalili
,
Ram Sarkar
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1007/s10489-021-02292-8
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1007/s10489-021-02292-8
The rapid spread of coronavirus disease has become an example of the worst disruptive disasters of the century around the globe. To fight against the spread of this virus, clinical image analysis of chest CT (computed tomography) images can play an important role for an accurate diagnostic. In the present work, a bi-modular hybrid model is proposed to detect COVID-19 from the chest CT images. In the first module, we have used a Convolutional Neural Network (CNN) architecture to extract features from the chest CT images. In the second module, we have used a bi-stage feature selection (FS) approach to find out the most relevant features for the prediction of COVID and non-COVID cases from the chest CT images. At the first stage of FS, we have applied a guided FS methodology by employing two filter methods: Mutual Information (MI) and Relief-F, for the initial screening of the features obtained from the CNN model. In the second stage, Dragonfly algorithm (DA) has been used for the further selection of most relevant features. The final feature set has been used for the classification of the COVID-19 and non-COVID chest CT images using the Support Vector Machine (SVM) classifier. The proposed model has been tested on two open-access datasets: SARS-CoV-2 CT images and COVID-CT datasets and the model shows substantial prediction rates of 98.39% and 90.0% on the said datasets respectively. The proposed model has been compared with a few past works for the prediction of COVID-19 cases. The supporting codes are uploaded in the Github link: https://github.com/Soumyajit-Saha/A-Bi-Stage-Feature-Selection-on-Covid-19-Dataset.
Related Topics
Concepts
Computer science
Artificial intelligence
Feature selection
Convolutional neural network
Support vector machine
Pattern recognition (psychology)
Coronavirus disease 2019 (COVID-19)
Feature (linguistics)
Classifier (UML)
Medicine
Pathology
Linguistics
Infectious disease (medical specialty)
Disease
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10489-021-02292-8
- https://link.springer.com/content/pdf/10.1007/s10489-021-02292-8.pdf
- OA Status
- hybrid
- Cited By
- 100
- References
- 82
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3155958087
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3155958087Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s10489-021-02292-8Digital Object Identifier
- Title
-
A bi-stage feature selection approach for COVID-19 prediction using chest CT imagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-04-19Full publication date if available
- Authors
-
Shibaprasad Sen, Soumyajit Saha, Somnath Chatterjee, Seyedali Mirjalili, Ram SarkarList of authors in order
- Landing page
-
https://doi.org/10.1007/s10489-021-02292-8Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s10489-021-02292-8.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s10489-021-02292-8.pdfDirect OA link when available
- Concepts
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Computer science, Artificial intelligence, Feature selection, Convolutional neural network, Support vector machine, Pattern recognition (psychology), Coronavirus disease 2019 (COVID-19), Feature (linguistics), Classifier (UML), Medicine, Pathology, Linguistics, Infectious disease (medical specialty), Disease, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
100Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 10, 2024: 19, 2023: 27, 2022: 24, 2021: 20Per-year citation counts (last 5 years)
- References (count)
-
82Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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