Automated COVID-19 Detection from WBC-DIFF Scattergram Images with Hybrid CNN Model Using Feature Selection Article Swipe
Hakan Ayyıldız
,
Mehmet Kalaycı
,
Seda Arslan Tuncer
,
Ahmet Çınar
,
Taner Tuncer
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.18280/ts.390206
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.18280/ts.390206
In the medical diagnosis such as WBC (white blood cell), the scattergram images show the relationships between neutrophils, eosinophils, basophils, lymphocytes, and monocytes cells in the blood. For COVID-19 detection, the distributions of these cells differ in healthy and COVID-19 patients. This study proposes a hybrid CNN model for COVID-19 detection using scatter images obtained from WBC sub (differential-DIFF) parameters instead of CT or X-Ray scans. As a data set, the scattergram images of 335 COVID-19 suspects without chronic disease, collected from the biochemistry department of Elazig Fethi Sekin City Hospital, are examined. At first, the data augmentation is performed by applying HSV(Hue, Saturation, Value) and CIE-1931(Commission Internationale de l'éclairage) conversions. Thus, three different image large sets are obtained as a result of raw, CIE-1931, and HSV conversions. Secondly, feature extraction is applied by giving these images as separate inputs to the CNN model. Finally, the ReliefF feature extraction algorithm is applied to determine the most dominant features in feature vectors and to determine the features that maximize classification accuracy. The obtaining feature vector is classified with high-performance SVM in binary classification. The overall accuracy is 95.2%, and the F1-Score is 94.1%. The results show that the method can successfully detect COVID -19 disease using scattergram images and is an alternative to CT and X-Ray scans.
Related Topics
Concepts
Pattern recognition (psychology)
Artificial intelligence
Support vector machine
Feature extraction
HSL and HSV
Feature selection
Convolutional neural network
Coronavirus disease 2019 (COVID-19)
Computer science
White blood cell
Feature (linguistics)
Mathematics
Medicine
Pathology
Immunology
Linguistics
Philosophy
Disease
Infectious disease (medical specialty)
Virus
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.18280/ts.390206
- https://www.iieta.org/download/file/fid/73670
- OA Status
- bronze
- Cited By
- 3
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4229452393
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4229452393Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.18280/ts.390206Digital Object Identifier
- Title
-
Automated COVID-19 Detection from WBC-DIFF Scattergram Images with Hybrid CNN Model Using Feature SelectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-30Full publication date if available
- Authors
-
Hakan Ayyıldız, Mehmet Kalaycı, Seda Arslan Tuncer, Ahmet Çınar, Taner TuncerList of authors in order
- Landing page
-
https://doi.org/10.18280/ts.390206Publisher landing page
- PDF URL
-
https://www.iieta.org/download/file/fid/73670Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
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https://www.iieta.org/download/file/fid/73670Direct OA link when available
- Concepts
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Pattern recognition (psychology), Artificial intelligence, Support vector machine, Feature extraction, HSL and HSV, Feature selection, Convolutional neural network, Coronavirus disease 2019 (COVID-19), Computer science, White blood cell, Feature (linguistics), Mathematics, Medicine, Pathology, Immunology, Linguistics, Philosophy, Disease, Infectious disease (medical specialty), VirusTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2022: 2Per-year citation counts (last 5 years)
- References (count)
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42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.pdf_url | https://www.iieta.org/download/file/fid/73670 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
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| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Traitement du Signal |
| primary_location.landing_page_url | https://doi.org/10.18280/ts.390206 |
| publication_date | 2022-04-30 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W4391285994, https://openalex.org/W2925080504, https://openalex.org/W3184618443, https://openalex.org/W2984802304, https://openalex.org/W3021622280, https://openalex.org/W3045874508, https://openalex.org/W3167203677, https://openalex.org/W3107806617, https://openalex.org/W3173092261, https://openalex.org/W3162351260, https://openalex.org/W3086039674, https://openalex.org/W3021001730, https://openalex.org/W3020650059, https://openalex.org/W3027682070, https://openalex.org/W3114166611, https://openalex.org/W3133191822, https://openalex.org/W3193707473, https://openalex.org/W3158225068, https://openalex.org/W3159395977, https://openalex.org/W3115913748, https://openalex.org/W3190059072, https://openalex.org/W3175141066, https://openalex.org/W2163605009, https://openalex.org/W6687483927, https://openalex.org/W2097117768, https://openalex.org/W2963163009, https://openalex.org/W3170064385, https://openalex.org/W3109469672, https://openalex.org/W3111092203, https://openalex.org/W1500895378, https://openalex.org/W2087347434, https://openalex.org/W3014003872, https://openalex.org/W3041173510, https://openalex.org/W3013843138, https://openalex.org/W3095688564, https://openalex.org/W3110281530, https://openalex.org/W3013507463, https://openalex.org/W2194775991, https://openalex.org/W2734349601, https://openalex.org/W3111797064, https://openalex.org/W3013019084, https://openalex.org/W3012817089 |
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| corresponding_author_ids | https://openalex.org/A5004319915 |
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| corresponding_institution_ids | https://openalex.org/I143396566 |
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