Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/1170
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Öztürk, Şaban | - |
dc.contributor.author | Özkaya, Umut | - |
dc.contributor.author | Barstuğan, Mücahid | - |
dc.date.accessioned | 2021-12-13T10:34:47Z | - |
dc.date.available | 2021-12-13T10:34:47Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 0899-9457 | - |
dc.identifier.issn | 1098-1098 | - |
dc.identifier.uri | https://doi.org/10.1002/ima.22469 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/1170 | - |
dc.description.abstract | Necessary screenings must be performed to control the spread of the COVID-19 in daily life and to make a preliminary diagnosis of suspicious cases. The long duration of pathological laboratory tests and the suspicious test results led the researchers to focus on different fields. Fast and accurate diagnoses are essential for effective interventions for COVID-19. The information obtained by using X-ray and Computed Tomography (CT) images is vital in making clinical diagnoses. Therefore it is aimed to develop a machine learning method for the detection of viral epidemics by analyzing X-ray and CT images. In this study, images belonging to six situations, including coronavirus images, are classified using a two-stage data enhancement approach. Since the number of images in the dataset is deficient and unbalanced, a shallow image augmentation approach was used in the first phase. It is more convenient to analyze these images with hand-crafted feature extraction methods because the dataset newly created is still insufficient to train a deep architecture. Therefore, the Synthetic minority over-sampling technique algorithm is the second data enhancement step of this study. Finally, the feature vector is reduced in size by using a stacked auto-encoder and principal component analysis methods to remove interconnected features in the feature vector. According to the obtained results, it is seen that the proposed method has leveraging performance, especially to make the diagnosis of COVID-19 in a short time and effectively. Also, it is thought to be a source of inspiration for future studies for deficient and unbalanced datasets. | en_US |
dc.language.iso | en | en_US |
dc.publisher | WILEY | en_US |
dc.relation.ispartof | INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | classification | en_US |
dc.subject | coronavirus | en_US |
dc.subject | COVID-19 | en_US |
dc.subject | feature extraction | en_US |
dc.subject | hand-crafted features | en_US |
dc.subject | sAE | en_US |
dc.title | Classification of Coronavirus (COVID-19) fromX-rayandCTimages using shrunken features | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1002/ima.22469 | - |
dc.identifier.pmid | PubMed: 32904960 | en_US |
dc.identifier.scopus | 2-s2.0-85089486925 | en_US |
dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü | en_US |
dc.authorid | Ozturk, Saban/0000-0003-2371-8173 | - |
dc.authorwosid | Ozturk, Saban/ABI-3936-2020 | - |
dc.identifier.volume | 31 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.startpage | 5 | en_US |
dc.identifier.endpage | 15 | en_US |
dc.identifier.wos | WOS:000560227500001 | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57191953654 | - |
dc.authorscopusid | 57191610477 | - |
dc.authorscopusid | 57200139642 | - |
dc.identifier.scopusquality | Q2 | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Article | - |
item.fulltext | With Fulltext | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
crisitem.author.dept | 02.04. Department of Electrical and Electronics Engineering | - |
Appears in Collections: | Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collections Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
Files in This Item:
File | Size | Format | |
---|---|---|---|
Int J Imaging Syst Tech - 2020 - zt rk - Classification of Coronavirus COVID‐19 from X‐ray and CT images using shrunken.pdf | 2.85 MB | Adobe PDF | View/Open |
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