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

Ahmet Saygılı

doi:10.35377/saucis...1309970

EN

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

Abstract

The 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.

Keywords

Supporting Institution

Research Projects Coordination Unit of Tekirdağ Namık Kemal University

Project Number

NKUBAP.06.GA.21.317

Thanks

This study was funded by the Scientific Research Projects Coordination Unit of Tekirdağ Namık Kemal University. Project number: NKUBAP.06.GA.21.317

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Details

Primary Language

English

Subjects

Empirical Software Engineering

Journal Section

Research Article

Authors

Ahmet Saygılı ^*
0000-0001-8625-4842
Türkiye

Early Pub Date

August 27, 2023

Publication Date

August 31, 2023

Submission Date

June 5, 2023

Acceptance Date

August 10, 2023

Published in Issue

Year 2023 Volume: 6 Number: 2

DOI

https://doi.org/10.35377/saucis...1309970

IZ

https://izlik.org/JA68ZC97LZ

Cite

RIS / Bibtex

APA

Saygılı, A. (2023). Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-ray Images. Sakarya University Journal of Computer and Information Sciences, 6(2), 123-139. https://doi.org/10.35377/saucis...1309970

AMA

1.Saygılı A. Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-ray Images. SAUCIS. 2023;6(2):123-139. doi:10.35377/saucis.1309970

Chicago

Saygılı, Ahmet. 2023. “Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-Ray Images”. Sakarya University Journal of Computer and Information Sciences 6 (2): 123-39. https://doi.org/10.35377/saucis. 1309970.

EndNote

Saygılı A (August 1, 2023) Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-ray Images. Sakarya University Journal of Computer and Information Sciences 6 2 123–139.

IEEE

[1]A. Saygılı, “Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-ray Images”, SAUCIS, vol. 6, no. 2, pp. 123–139, Aug. 2023, doi: 10.35377/saucis...1309970.

ISNAD

Saygılı, Ahmet. “Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-Ray Images”. Sakarya University Journal of Computer and Information Sciences 6/2 (August 1, 2023): 123-139. https://doi.org/10.35377/saucis. 1309970.

JAMA

1.Saygılı A. Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-ray Images. SAUCIS. 2023;6:123–139.

MLA

Saygılı, Ahmet. “Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-Ray Images”. Sakarya University Journal of Computer and Information Sciences, vol. 6, no. 2, Aug. 2023, pp. 123-39, doi:10.35377/saucis. 1309970.

Vancouver

1.Ahmet Saygılı. Rapid and Precise Identification of COVID-19 through Segmentation and Classification of CT and X-ray Images. SAUCIS. 2023 Aug. 1;6(2):123-39. doi:10.35377/saucis. 1309970

Öz

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

Abstract

Keywords

Supporting Institution

Project Number

Thanks

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite