TRANSFORMER BASED COVID-19 DETECTION USING CHEST X-RAYS

Hacı Ömer Dokumacı

doi:10.17780/ksujes.1395475

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TRANSFORMER BASED COVID-19 DETECTION USING CHEST X-RAYS

Abstract

Covid-19 has affected millions globally, leading to substantial illness and mortality. Chest X-rays serve as a rapid and effective means of tracking the progression of Covid-19. However, diagnosing Covid-19 from a chest X-ray can be complex, and even skilled radiologists may not always provide a conclusive diagnosis. In our research, we utilized a dataset comprising X-ray images of Covid-19, lung opacity, viral pneumonia, and healthy patients to assess the efficacy of various vision transformer-based models. A modified version of the Swin Transformer achieved an accuracy of 98.9% and a precision of 99.2% on Covid-19 images in a four-way classification task. Our findings are competitive with cutting-edge techniques for diagnosing Covid-19. This method could aid healthcare professionals in screening patients for Covid-19, thereby enabling quicker treatment and improved health outcomes for those affected by the virus.

Keywords

References

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Details

Primary Language

English

Subjects

Deep Learning , Modelling and Simulation

Journal Section

Research Article

Authors

Hacı Ömer Dokumacı ^*
0000-0003-4032-0669
Türkiye

Publication Date

September 3, 2024

Submission Date

November 24, 2023

Acceptance Date

November 30, 2023

Published in Issue

Year 2024 Volume: 27 Number: 3

DOI

https://doi.org/10.17780/ksujes.1395475

IZ

https://izlik.org/JA35FY33ZU

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RIS / Bibtex

APA

Dokumacı, H. Ö. (2024). TRANSFORMER BASED COVID-19 DETECTION USING CHEST X-RAYS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(3), 679-687. https://doi.org/10.17780/ksujes.1395475

TRANSFORMER BASED COVID-19 DETECTION USING CHEST X-RAYS

AKCİĞER GÖRÜNTÜLERİNDEN TRANSFORMER TABANLI COVID-19 TESPİTİ

Öz

Anahtar Kelimeler

TRANSFORMER BASED COVID-19 DETECTION USING CHEST X-RAYS

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite