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AKCİĞER GÖRÜNTÜLERİNDEN TRANSFORMER TABANLI COVID-19 TESPİTİ

Year 2024, Volume: 27 Issue: 3, 679 - 687, 03.09.2024
https://doi.org/10.17780/ksujes.1395475

Abstract

Covid-19, küresel olarak milyonlarca kişiyi etkileyerek önemli hastalıklara ve ölümlere yol açmıştır. Akciğer röntgenleri, Covid-19’un ilerlemesini izlemek için hızlı ve etkili bir yöntem olarak hizmet vermektedir. Ancak, bir akciğer röntgeninden Covid-19’u teşhis etmek karmaşık olabilir ve hatta deneyimli radyologlar bile her zaman kesin bir teşhis koyamayabilir. Bu araştırmada, Covid-19, akciğer opasitesi, viral pnömoni ve sağlıklı hastaların X-ray görüntülerinden oluşan bir veri setini kullanarak çeşitli vision transformer tabanlı modellerin etkinliği değerlendirildi. Swin Transformer’ın modifiye edilmiş bir versiyonu, Covid-19 görüntülerinde dört yönlü sınıflandırmada %98.9 doğruluk ve %99.2 hassasiyet elde etti. Bulgularımız, Covid-19 teşhisi için, son teknoloji tekniklerle rekabet edebilecek düzeydedir. Bu yöntem, sağlık profesyonellerinin Covid-19 için hastaları taramasına yardımcı olabilir, böylece daha hızlı tedavi sağlanabilir ve Covid-19 hastaları için daha iyi sağlık sonuçları elde edilebilir

References

  • Apostolopoulos, I. D. & Mpesiana, T. A. (2020). COVID-19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 43(2), 635-640.
  • Chetoui, M., & Akhlouf, M. A. (2022). Explainable vision transformers and radiomics for covid-19 detection in chest x-rays. J. Clin. Med. 11, 3013.
  • Chowdhury, M.E., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M.A., Mahbub, Z.B., Islam, K.R., Khan, M.S., Iqbal, A., Emadi, N.A., et al. (2020). Can AI help in screening viral and covid-19 pneumonia? https://arxiv.org/abs/2003.13145.
  • Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. https://arxiv.org/abs/2010.11929.
  • Gorbalenya, A. E., Baker, S. C., Baric, R. S., De Groot, R. J., Drosten, C., Gulyaeva, A. A & Ziebuhr, J. (2020). The species severe acute respiratory syndrome-related coronavirus: Classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol; 5, 536–44.
  • Khan, A. I., Shah, J. L. & Bhat, M. M. (2020). CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer Methods & Programs in Biomedicine, 196(26), 105581.
  • Kucirka, L. M., Lauer, S. A., Laeyendecker, O., Boon, D. & Lessler, J. (2020). Variation in false-negative rate of reverse transcriptase polymerase chain reaction-based SARS-CoV-2 tests by time Since exposure. Annals of Internal Medicine, 173(4), 262–267.
  • Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. https://arxiv.org/abs/2103.14030.
  • Okolo, G. I., Katsigiannis, S. & Ramzan, N. (2022). Ievit: An enhanced vision transformer architecture for chest x-ray image classification. Comput. Methods Programs Biomed. 226, 107141.
  • Phelan, A. L., Katz, R. & Gostin, L. O. (2020). The novel coronavirus originating in Wuhan, China: Challenges for global health governance. JAMA, 323(8), 709– 710.
  • Rahhal, A.M.M. et al. (2022). Covid-19 detection in ct/x-ray imagery using vision transformers. J. Personal. Med. 12(2), 310.
  • Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S.B.A., Islam, S.T., Maadeed, S.A., Zughaier, S.M., Khan, M.S., et al. (2021). Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection Using Chest X-ray Images. Computers in Biology and Medicine, 132, 104319.
  • Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., & Li, Y. (2022). MaxViT: Multi-Axis Vision Transformer. https://arxiv.org/abs/2204.01697.
  • Umer, M., Ashraf, I., Ullah, S., Mehmood, A., & Choi, G.S. (2022). Covinet: A convolutional neural network approach for predicting covid-19 from chest x-ray images. Journal of Ambient Intelligence and Humanized Computing, 13(1), 535–547.
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need. https://arxiv.org/abs/1706.03762.
  • Wang, C., Horby, P. W., Hayden, F. G. & Gao, G. F. (2020) A novel coronavirus outbreak of global health concern. Lancet (London, England), 395(10223), 470– 473.
  • Wang, L., Lin Z. Q., & Wong, A. (2020). Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 10(1), 1–12.
  • Wu, B., Xu, C., Dai, X., Wan, A., Zhang, P., Yan, Z., Tomizuka, M., Gonzalez, J., Keutzer, K., & Vajda, P. (2020). Visual Transformers: Token-based Image Representation and Processing for Computer Vision. https://arxiv.org/abs/2006.03677.
  • Yang, H., Wang, L., Xu, Y., & Liu, X. (2023). Covidvit: A novel neural network with self-attention mechanism to detect covid-19 through x-ray images. International Journal of Machine Learning and Cybernetics, 14, 973–987.

TRANSFORMER BASED COVID-19 DETECTION USING CHEST X-RAYS

Year 2024, Volume: 27 Issue: 3, 679 - 687, 03.09.2024
https://doi.org/10.17780/ksujes.1395475

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.

References

  • Apostolopoulos, I. D. & Mpesiana, T. A. (2020). COVID-19: Automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine, 43(2), 635-640.
  • Chetoui, M., & Akhlouf, M. A. (2022). Explainable vision transformers and radiomics for covid-19 detection in chest x-rays. J. Clin. Med. 11, 3013.
  • Chowdhury, M.E., Rahman, T., Khandakar, A., Mazhar, R., Kadir, M.A., Mahbub, Z.B., Islam, K.R., Khan, M.S., Iqbal, A., Emadi, N.A., et al. (2020). Can AI help in screening viral and covid-19 pneumonia? https://arxiv.org/abs/2003.13145.
  • Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. https://arxiv.org/abs/2010.11929.
  • Gorbalenya, A. E., Baker, S. C., Baric, R. S., De Groot, R. J., Drosten, C., Gulyaeva, A. A & Ziebuhr, J. (2020). The species severe acute respiratory syndrome-related coronavirus: Classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol; 5, 536–44.
  • Khan, A. I., Shah, J. L. & Bhat, M. M. (2020). CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Computer Methods & Programs in Biomedicine, 196(26), 105581.
  • Kucirka, L. M., Lauer, S. A., Laeyendecker, O., Boon, D. & Lessler, J. (2020). Variation in false-negative rate of reverse transcriptase polymerase chain reaction-based SARS-CoV-2 tests by time Since exposure. Annals of Internal Medicine, 173(4), 262–267.
  • Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. https://arxiv.org/abs/2103.14030.
  • Okolo, G. I., Katsigiannis, S. & Ramzan, N. (2022). Ievit: An enhanced vision transformer architecture for chest x-ray image classification. Comput. Methods Programs Biomed. 226, 107141.
  • Phelan, A. L., Katz, R. & Gostin, L. O. (2020). The novel coronavirus originating in Wuhan, China: Challenges for global health governance. JAMA, 323(8), 709– 710.
  • Rahhal, A.M.M. et al. (2022). Covid-19 detection in ct/x-ray imagery using vision transformers. J. Personal. Med. 12(2), 310.
  • Rahman, T., Khandakar, A., Qiblawey, Y., Tahir, A., Kiranyaz, S., Kashem, S.B.A., Islam, S.T., Maadeed, S.A., Zughaier, S.M., Khan, M.S., et al. (2021). Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection Using Chest X-ray Images. Computers in Biology and Medicine, 132, 104319.
  • Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., & Li, Y. (2022). MaxViT: Multi-Axis Vision Transformer. https://arxiv.org/abs/2204.01697.
  • Umer, M., Ashraf, I., Ullah, S., Mehmood, A., & Choi, G.S. (2022). Covinet: A convolutional neural network approach for predicting covid-19 from chest x-ray images. Journal of Ambient Intelligence and Humanized Computing, 13(1), 535–547.
  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., & Polosukhin, I. (2017). Attention Is All You Need. https://arxiv.org/abs/1706.03762.
  • Wang, C., Horby, P. W., Hayden, F. G. & Gao, G. F. (2020) A novel coronavirus outbreak of global health concern. Lancet (London, England), 395(10223), 470– 473.
  • Wang, L., Lin Z. Q., & Wong, A. (2020). Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 10(1), 1–12.
  • Wu, B., Xu, C., Dai, X., Wan, A., Zhang, P., Yan, Z., Tomizuka, M., Gonzalez, J., Keutzer, K., & Vajda, P. (2020). Visual Transformers: Token-based Image Representation and Processing for Computer Vision. https://arxiv.org/abs/2006.03677.
  • Yang, H., Wang, L., Xu, Y., & Liu, X. (2023). Covidvit: A novel neural network with self-attention mechanism to detect covid-19 through x-ray images. International Journal of Machine Learning and Cybernetics, 14, 973–987.
There are 19 citations in total.

Details

Primary Language English
Subjects Deep Learning, Modelling and Simulation
Journal Section Electrical and Electronics Engineering
Authors

Hacı Ömer Dokumacı 0000-0003-4032-0669

Publication Date September 3, 2024
Submission Date November 24, 2023
Acceptance Date November 30, 2023
Published in Issue Year 2024Volume: 27 Issue: 3

Cite

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