Araştırma Makalesi

Evaluation of Poor Prognosis in rRT-PCR Positive Covid-19 Cases with Using Deep Transfer Learning Network

Cilt: 5 Sayı: 2 18 Temmuz 2022
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Evaluation of Poor Prognosis in rRT-PCR Positive Covid-19 Cases with Using Deep Transfer Learning Network

Öz

The infection called Covid-19 caused by the new type of coronavirus (SARS-CoV-2) is an epidemic and deadly disease that spreads rapidly all over the world. Early detection of Covid-19 will enable the patient to receive appropriate treatment and increase the chance of survival. In this study, it is aimed to investigate the detection of poor prognosis from chest CT images in Covid-19 patients who died and healed using deep learning. For this purpose, a dataset containing a total of 5997 CT images were used and images were classified using the Inception-V3. In order to evaluate the classifier ROC curves are drawn, AUC and accuracy values are used as performance metrics. Inception-V3 model was run 10 times, and a maximum classification performance of 97,55% and an average of 97,01% was achieved. The classification results prove that Inception-V3 can classify CT images with a high accuracy rate for evaluation of Covid-19 prognosis.

Anahtar Kelimeler

Kaynakça

  1. Baraboshkin, E.E., Ismailova, L.S., Orlov, D.M., Zhukovskaya, E.A., Kalmykov, G.A., Khotylev, O.V., Baraboshkin, E.Y. and Koroteev, D.A. Deep convolutions for in-depth automated rock typing. Computers & Geosciences 2020; 135: 104330.
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  3. Covid-19 Guide. https://hsgm.saglik.gov.tr/depo/birimler/goc_sagligi/covid19/rehber/COVID-19_Rehberi20200414_eng_v4_002_14.05.2020.pdf, (last accessed date: 15.11.2021)
  4. Del Valle, D.M., Kim-Schulze, S., Huang, H.H., Beckmann, N.D., Nirenberg, S., Wang, B., Lavin, Y., Swartz, T.H., Madduri, D., Stock, A., Marron, T.U., Xie, H., Patel, M., Tuballes, K., Van Oekelen, O., Rahman, A., Kovatch, P., Aberg, J.A., Schadt, E., Jagannath, S., Mazumdar, M. et al., 2020, “An inflammatory cytokine signature predicts COVID-19 severity and survival”. Nature Medicine, 26(10), 1636-1643.
  5. Fawcett, T. An introduction to ROC analysis. Pattern Recognition Letters 2006; 27(8): 861-874.
  6. Goodfellow, I., Bengio, Y. and Courville, A. Deep Learning. MIT Press. 2016.
  7. Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S. and Lew, M.S. Deep learning for visual understanding: A review. Neurocomputing 2016; 187: 27-48.
  8. Kirienko, M., Ninatti, G., Cozzi, L., Voulaz, E., Gennaro, N., Barajon, I., Ricci, F., Carlo-Stella, C., Zucali, P., Sollini, M., Balzarini, L. and Chiti, A. Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas. La Radiologia Medica 2020; 125(10): 951-960.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Bilgisayar Yazılımı

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

18 Temmuz 2022

Gönderilme Tarihi

17 Kasım 2021

Kabul Tarihi

2 Mart 2022

Yayımlandığı Sayı

Yıl 2022 Cilt: 5 Sayı: 2

Kaynak Göster

APA
Şalk, İ., Polat, Ö., & Hasbek, M. (2022). Evaluation of Poor Prognosis in rRT-PCR Positive Covid-19 Cases with Using Deep Transfer Learning Network. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 5(2), 505-521. https://doi.org/10.47495/okufbed.1024845
AMA
1.Şalk İ, Polat Ö, Hasbek M. Evaluation of Poor Prognosis in rRT-PCR Positive Covid-19 Cases with Using Deep Transfer Learning Network. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2022;5(2):505-521. doi:10.47495/okufbed.1024845
Chicago
Şalk, İsmail, Özlem Polat, ve Mürşit Hasbek. 2022. “Evaluation of Poor Prognosis in rRT-PCR Positive Covid-19 Cases with Using Deep Transfer Learning Network”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 5 (2): 505-21. https://doi.org/10.47495/okufbed.1024845.
EndNote
Şalk İ, Polat Ö, Hasbek M (01 Temmuz 2022) Evaluation of Poor Prognosis in rRT-PCR Positive Covid-19 Cases with Using Deep Transfer Learning Network. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 5 2 505–521.
IEEE
[1]İ. Şalk, Ö. Polat, ve M. Hasbek, “Evaluation of Poor Prognosis in rRT-PCR Positive Covid-19 Cases with Using Deep Transfer Learning Network”, Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 5, sy 2, ss. 505–521, Tem. 2022, doi: 10.47495/okufbed.1024845.
ISNAD
Şalk, İsmail - Polat, Özlem - Hasbek, Mürşit. “Evaluation of Poor Prognosis in rRT-PCR Positive Covid-19 Cases with Using Deep Transfer Learning Network”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 5/2 (01 Temmuz 2022): 505-521. https://doi.org/10.47495/okufbed.1024845.
JAMA
1.Şalk İ, Polat Ö, Hasbek M. Evaluation of Poor Prognosis in rRT-PCR Positive Covid-19 Cases with Using Deep Transfer Learning Network. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 2022;5:505–521.
MLA
Şalk, İsmail, vd. “Evaluation of Poor Prognosis in rRT-PCR Positive Covid-19 Cases with Using Deep Transfer Learning Network”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 5, sy 2, Temmuz 2022, ss. 505-21, doi:10.47495/okufbed.1024845.
Vancouver
1.İsmail Şalk, Özlem Polat, Mürşit Hasbek. Evaluation of Poor Prognosis in rRT-PCR Positive Covid-19 Cases with Using Deep Transfer Learning Network. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 01 Temmuz 2022;5(2):505-21. doi:10.47495/okufbed.1024845

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