Research Article
Year 2024,
, 679 - 687, 03.09.2024
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
Keywords
References
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Year 2024,
, 679 - 687, 03.09.2024
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
- 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 | |
| Publication Date | September 3, 2024 |
| Submission Date | November 24, 2023 |
| Acceptance Date | November 30, 2023 |
| Published in Issue | Year 2024 |