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Journals / Sakarya University Journal of Computer and Information Sciences / 2023 / Cilt: 6 Sayı: 2

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

Ahmet SAYGILI
Pages
123–139
Publication Date
2023-08-31
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

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Objective & Hypothesis:The COVID-19 pandemic, caused by a novel coronavirus, has become a global epidemic.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:COVID-19 · Diagnosis · Imaging techniques

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.

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Cite This Article

SAYGILI, 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, Cilt: 6 Sayı: 2, 123–139. https://ajindex.com/articles/961441

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References (1)

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