KaydedilenlerDerginizi ekleyin

Dergiler / Aurum Journal of Health Sciences / 2026 / Cilt: 4 Sayı: 1

DETECTION OF COVID-19 PNEUMONIA EFFECTS IN CHEST X-RAYS USING DEEP LEARNING

Mohammed Khamees Ahmed(ALTINBAŞ ÜNİVERSİTESİ)0000-0001-9145-1939
Sayfa
25–33
Yayın Tarihi
2022-04-26
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In this paper we propose a deep learning based model for COVID-19 detection relying on the effects it yields on the lungs.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:The development of technological tools based on artificial intelligence (AI) could contribute significantly in the fight against COVID-19.
📊
Temel Bulgular & Veri:AI is the ability of a machine to apply human cognitive functions.
💡
Sonuç & Katkı:In this paper we propose a deep learning based model for COVID-19 detection relying on the effects it yields on the lungs.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:MRI · XRAYS · COVID-19

Özet

The development of technological tools based on artificial intelligence (AI) could contribute significantly in the fight against COVID-19. AI is the ability of a machine to apply human cognitive functions. In this paper we propose a deep learning based model for COVID-19 detection relying on the effects it yields on the lungs.

🔖

Bu Makaleye Atıf Yap

Ahmed, M. K. (2022). DETECTION OF COVID-19 PNEUMONIA EFFECTS IN CHEST X-RAYS USING DEEP LEARNING. Aurum Journal of Health Sciences, Cilt: 4 Sayı: 1, 25–33. https://ajindex.com/articles/1581114

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (21)

  1. Afshar, P., S. Heidarian, F. Naderkhani, A. Oikonomou, K.N. Plataniotis, and A. Mohammadi. 2020. Covid- caps: a capsule network-based framework for identification of covid-19 cases from x-ray images. arXiv preprint arXiv:2004.02696
  2. Ajlan, A.M., R.A. Ahyad, L.G. Jamjoom, A. Alharthy, and T.A. Madani. 2014. Middle East respiratory syndrome coronavirus (MERSCoV) infection: chest CT findings. Am J Roentgenol 203(4):782– 787
  3. Apostolopoulos, I.D., and T.A. Mpesiana. 2020. Covid-19: automatic detection from x-ray images utilising transfer learning with convolutional neural networks. Phys Eng Sci Med:1
  4. Basile, C., C. Combe, F. Pizzarelli, A. Covic, A. Davenport, M. Kanbay, D. Kirmizis, D. Schneditz, F. van der Sande, and S. Mitra. 2020. Recommendations for the prevention, mitigation and containment of the emerging SARS-CoV-2 (COVID-19) pandemic in haemodialysis centres. Nephrol Dialysis Transplantation 35:737– 741
  5. Chen, S.-F., Y.-C. Chen, C.-K. Yeh, F. Wang, and C. Yu. 2017. Order-free rnn with visual attention for multi- label classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
  6. Diamond, S., V. Sitzmann, S. Boyd, G. Wetzstein, and F. Heide. 2017. Dirty Pixels: Optimizing Image Classification Architectures for Raw Sensor Data. arXiv:1701.06487
  7. Dodge, S., and L. Karam. 2016. Understanding how image quality affects deep neural networks. In: Quality of Multimedia Experience (QoMEX), Eighth International Conference on, IEEE, arXiv:1604.04004v2, pp. 1–6
  8. Hansell, D.M., A.A. Bankier, H. MacMahon, T.C. McLoud, N.L. Muller, and J. Remy. 2008. Fleischner society: glossary of terms for thoracic imaging. Radiology 246(3):697–722
  9. Huang, C., Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, L. Zhang, G. Fan, J. Xu, X. Gu, and Z. Cheng. 2020. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223):497– 506
  10. Kanne, J.P. 2020. Chest CT findings in 2019 novel coronavirus (2019-nCoV) infections from Wuhan, China: key points for the radiologist
  11. Koo, H.J., S. Lim, J. Choe, S.H. Choi, H. Sung, and K.H. Do. 2018. Radiographic and CT features of viral pneumonia. Radiographics 38(3):719–739 7.
  12. Li, L., L. Qin, Z. Xu, Y. Yin, X. Wang, B. Kong, J. Bai, Y. Lu, Z. Fang, Q. Song, and K. Cao. 2020. Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest ct. Radiology: 200905
  13. Narin, A., C. Kaya, and Z. Pamuk. 2020. Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849
  14. Ooi, G.C., P.L. Khong, N.L. Müller, W.C. Yiu, L.J. Zhou, J.C. Ho, B. Lam, S. Nicolaou, and K.W. Tsang. 2004. Severe acute respiratory syndrome: temporal lung changes at thin-section CT in 30 patients. Radiology 230(3):836–844
  15. Roosa, K., Y. Lee, R. Luo, A. Kirpich, R. Rothenberg, J.M. Hyman, P. Yan, and G. Chowell. 2020. Realtime forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infect Dis Modell 5:256–263
  16. Wang, Y., M. Hu, Q. Li, X.P. Zhang, G. Zhai, and N. Yao. 2020. Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner. arXiv preprint arXiv:2002.05534
  17. Wong, K.T., G.E. Antonio, D.S. Hui, N. Lee, E.H. Yuen, A. Wu, C.B. Leung, T.H. Rainer, P. Cameron, S.S. Chung, and J.J. Sung. 2003. Severe acute respiratory syndrome: radiographic appearances and pattern of progression in 138 patients. Radiology 228(2):401–406
  18. Xie, X., X. Li, S. Wan, and Y. Gong. 2006. Mining x-ray images of SARS patients. In: Data Mining. Springer, Berlin, pp 282–294
  19. Xu, B., X.A. Meng. 2020. Deep learning algorithm using CT images to screen for Corona Virus Disease (COVID- 19)
  20. Yan, L., H.T. Zhang, Y. Xiao, M. Wang, C. Sun, J. Liang, S. Li, M. Zhang, Y. Guo, Y. Xiao, and X. Tang. 2020. Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan. medRxiv
  21. Yao, L., E. Poblenz, D. Dagunts, B. Covington, D. Bernard, and K. Lyman. 2017. Learning to diagnose from scratch by exploiting dependencies among labels. arXiv preprint arXiv:1710.10501