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

Year 2024, , 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, , 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 2024

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