Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-ray Images

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dc.contributor.authorSaygılı, Ahmet
dc.date.accessioned2024-10-29T17:53:18Z
dc.date.available2024-10-29T17:53:18Z
dc.date.issued2023
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractThe COVID-19 pandemic, caused by a novel coronavirus, has become a global epidemic. Although the reverse transcription-polymerase chain reaction (RT-PCR) test is the current gold standard for detecting the virus, its low reliability has led to the use of CT and X-ray imaging in diagnostics. As limited vaccine availability necessitates rapid and accurate detection, this study applies k-means and fuzzy c-means segmentation to CT and X-ray images to classify COVID-19 cases as either diseased or healthy for CT scans and diseased, healthy, or non-COVID pneumonia for X-rays. Our research employs four open-access, widely-used datasets and is conducted in four stages: preprocessing, segmentation, feature extraction, and classification. During feature extraction, we employ the Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Histogram of Oriented Gradients (HOG). In the classification process, our approach involves utilizing k-Nearest Neighbor (kNN), Support Vector Machines (SVM), and Extreme Learning Machines (ELM) techniques. Our research achieved a sensitivity rate exceeding 99%, which is higher than the 60-70% sensitivity rate of PCR tests. As a result, our study can serve as a decision support system that can help medical professionals make rapid and precise diagnoses with a high level of sensitivity.
dc.identifier.doi10.35377/saucis...1309970
dc.identifier.endpage139
dc.identifier.issn2636-8129
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85214837216
dc.identifier.startpage123
dc.identifier.trdizinid1195125
dc.identifier.urihttps://doi.org/10.35377/saucis...1309970
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1195125
dc.identifier.urihttps://hdl.handle.net/20.500.11776/13499
dc.identifier.volume6
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofSakarya University Journal of Computer and Information Sciences (Online)
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectCOVID-19
dc.subjectDiagnosis
dc.subjectImaging techniques
dc.subjectSegmentation methods
dc.subjectMachine learning-based classification
dc.titleRapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-ray Images
dc.typeArticle

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