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DERİN ÖĞRENME TABANLI MODELLERLE AKCİĞER X-RAY GÖRÜNTÜLERİNDEN COVID-19 TESPİTİ

Year 2024, Volume: 27 Issue: 2, 481 - 487, 03.06.2024

Hacı Ömer Dokumacı

https://doi.org/10.17780/ksujes.1395468

Abstract

COVID-19 dünya çapında milyonlarca insanı enfekte etmiştir ve önemli hastalık ve ölümlere neden olmuştur. Akciğer röntgeni (CXR), COVID-19 hastalığını izlemek için hızlı ve etkili bir yöntemdir. CXR taramasından COVID-19 teşhisi zor olabilir ve deneyimli radyologlar bile her durumda kesin bir teşhis koyamayabilir. Bu çalışmada, çeşitli CNN tabanlı modellerin performansını değerlendirmek için COVID-19, akciğer opaklığı ve viral pnömonisi olan hastaların X-ışını görüntülerinden oluşan bir veri seti kullanıldı. Değiştirilmiş bir ConvNext’le, 4 yönlü sınıflandırmada COVID-19 görüntülerinde %98,1 doğruluk ve %97,8 kesinlik elde edildi. ConvNext, COVID-19 teşhisi için kullanılan en son tekniklere göre iyi bir performans sergilemektedir. Bu çalışmada ortaya konulan yöntem, klinisyenleri COVID-19 hastalarını taramada destekleyebilir. Böylece bu hastalar için daha hızlı tedavi ve daha iyi sağlık sonuçları mümkün olabilir.

Keywords

Covid-19 Derin öğrenme CNN

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.
  • Arias-Garzo´n, D., Alzate-Grisales, J.A., Orozco-Arias, S., Arteaga-Arteaga, H.B., Bravo-Ortiz, M.A., MoraRubio, A., vd. (2021). COVID-19 detection in X-ray images using convolutional neural networks. Mach Learn Appl, 6, 100138.
  • 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., vd. (2020). Can AI help in screening viral and covid-19 pneumonia?. https://arxiv.org/abs/2003.13145.
  • 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.
  • He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. https://arxiv.org/abs/1512.03385.
  • 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., Mao, H., Wu, C., Feichtenhofer, C., Darrell, T., & Xie, S. (2022). A ConvNet for the 2020s. https://arxiv.org/abs/2201.03545.
  • Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., & Jamalipour Soufi, G. (2020). Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning. Med Image Anal, 65, 101794.
  • Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., & Rajendra Acharya, U. (2020). Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, 121, 103792.
  • 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.
  • 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., & Chowdhury, M.E. (2021). Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection Using Chest X-ray Images. Computers in Biology and Medicine, 132, 104319.
  • Sethy, P.K., Behera, S.K., Ratha, P.K., & Biswas P. (2020). Detection of coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine. Int J Math Eng Manag Sci, 5(4), 643-651.
  • Tan, M., & Le, Q.V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. https://arxiv.org/abs/1905.11946.
  • 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.
  • 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.
  • 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. Sci Rep, 10(1), 19549.
  • Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2016). Aggregated Residual Transformations for Deep Neural Networks. https://arxiv.org/abs/1611.05431.

DETECTION OF COVID-19 FROM CHEST X-RAY IMAGES WITH DEEP LEARNING BASED MODELS

Year 2024, Volume: 27 Issue: 2, 481 - 487, 03.06.2024

Hacı Ömer Dokumacı

https://doi.org/10.17780/ksujes.1395468

Abstract

COVID-19 has infected millions of people worldwide and caused significant illness and death. Chest X-rays are a quick and efficient method for monitoring COVID-19 disease. The diagnosis of COVID-19 from a CXR scan can be challenging, and even experienced radiologists may not be able to make a definitive diagnosis in all cases. In this study, we used a dataset of X-ray images of COVID-19, lung opacity, viral pneumonia, and healthy patients to evaluate the performance of several CNN-based models. A modified ConvNext has achieved 98.1% accuracy and 97.8% precision on COVID-19 images in a 4-way classification effort. Our results compare well with state-of-the-art techniques for COVID-19 diagnosis. Our approach could support clinicians in screening patients for COVID-19, thus facilitating faster treatment and better health outcomes for COVID-19 patients.

Keywords

Covid-19 Deep learning CNN

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.
  • Arias-Garzo´n, D., Alzate-Grisales, J.A., Orozco-Arias, S., Arteaga-Arteaga, H.B., Bravo-Ortiz, M.A., MoraRubio, A., vd. (2021). COVID-19 detection in X-ray images using convolutional neural networks. Mach Learn Appl, 6, 100138.
  • 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., vd. (2020). Can AI help in screening viral and covid-19 pneumonia?. https://arxiv.org/abs/2003.13145.
  • 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.
  • He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. https://arxiv.org/abs/1512.03385.
  • 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., Mao, H., Wu, C., Feichtenhofer, C., Darrell, T., & Xie, S. (2022). A ConvNet for the 2020s. https://arxiv.org/abs/2201.03545.
  • Minaee, S., Kafieh, R., Sonka, M., Yazdani, S., & Jamalipour Soufi, G. (2020). Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning. Med Image Anal, 65, 101794.
  • Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., & Rajendra Acharya, U. (2020). Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, 121, 103792.
  • 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.
  • 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., & Chowdhury, M.E. (2021). Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection Using Chest X-ray Images. Computers in Biology and Medicine, 132, 104319.
  • Sethy, P.K., Behera, S.K., Ratha, P.K., & Biswas P. (2020). Detection of coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine. Int J Math Eng Manag Sci, 5(4), 643-651.
  • Tan, M., & Le, Q.V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. https://arxiv.org/abs/1905.11946.
  • 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.
  • 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.
  • 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. Sci Rep, 10(1), 19549.
  • Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2016). Aggregated Residual Transformations for Deep Neural Networks. https://arxiv.org/abs/1611.05431.
There are 19 citations in total.

Details

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

Hacı Ömer Dokumacı 0000-0003-4032-0669

Publication Date June 3, 2024
Submission Date November 24, 2023
Acceptance Date November 30, 2023
Published in Issue Year 2024Volume: 27 Issue: 2

Cite

APA Dokumacı, H. Ö. (2024). DERİN ÖĞRENME TABANLI MODELLERLE AKCİĞER X-RAY GÖRÜNTÜLERİNDEN COVID-19 TESPİTİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(2), 481-487. https://doi.org/10.17780/ksujes.1395468
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