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Year 2021, Volume: 25 Issue: 3, 800 - 810, 30.06.2021
https://doi.org/10.16984/saufenbilder.903886

Abstract

References

  • [1] S.E. Park, “Epidemiology, virology, and clinical features of severe acute respiratory syndrome - coronavirus-2 (SARS-CoV-2; Coronavirus Disease-19)”, Clin Exp Pediatr vol. 63, no. 4, pp. 119-124, 2020. doi:10.3345/cep.2020.00493
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  • [7] T. Ai, Z. Yang, H. Hou, C. Zhan, C. Chen et al., “Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases”, Radiology, vol. 296, no. 2, pp. E32-E40, 2020. doi:10.1148/radiol.2020200642
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Detection of Covid-19 from Chest CT Images using Xception Architecture: A Deep Transfer Learning based Approach

Year 2021, Volume: 25 Issue: 3, 800 - 810, 30.06.2021
https://doi.org/10.16984/saufenbilder.903886

Abstract

Covid-19 infection, which first appeared in Wuhan, China in December 2019, affected the whole world in a short time like three months. The disease caused by the virus called SARS-CoV-2 affects many organs, especially the lungs, brain, liver and kidney, and causes a large number of deaths. Early detection of Covid-19 using computer-aided methods will ensure that the patient reaches the right treatment without wasting time, and the spread of the disease will be controlled. This study proposes a solution for detecting Covid-19 using chest computed tomography (CT) scan images. Firstly, image features are extracted using Xception network, convolutional neural network (CNN) based transfer learning architecture, then classification process is performed with a fully connected neural network (FCNN) added at the end of this architecture. The classification model was tested ten times on the publicly available SARS-CoV-2-CT-scan dataset containing 2482 CT images labelled as covid and non-covid. The precision, recall, f1-score and accuracy metrics were used as performance measures. While obtaining an average of 98.89% accuracy, in the best case, 99.59% classification performance was achieved. Xception outperforms other methods in the literature. The results promise that the proposed method can be evaluated as a clinical option helping experts in the detection of Covid-19 from CT images.

References

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  • [16] L. Wang, Z.Q. Lin, A. Wong, “COVID-Net: A tailored deep convolutional neural network design for detection of Covid-19 cases from chest radiography images”, arXiv, arXiv:2003.09781, 2020. https://arxiv.org/abs/2003.09871
  • [17] P.K. Sethy, S.K. Behera, P.K. Ratha, P. Biswas, “Detection of coronavirus disease (Covid-19) based on deep features ansd support vector machine”, International Journal of Mathematical, Engineering and Management Sciences, vol. 4, no. 5, pp. 642-651, 2020. doi:10.33889/IJMEMS.2020.5.4.052
  • [18] E.E.D. Hemdan, M.A. Shouman, M.E. Karar, “COVIDX-Net: A framework of deep learning classifiers to diagnose Covid-19 in X-ray images”, arXiv, arXiv:2003.11055, 2020. https://arxiv.org/abs/2003.11055
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  • [20] S. Ying, S. Zheng, L. Li, X. Zhang, X. Zhang et al., “Deep learning enables accurate diagnosis of novel coronavirus (Covid 19) with CT images”, medRxiv, 2020. doi:10.1101/2020.02.23.20026930
  • [21] S. Wang, B. Kang, J. Ma, X. Zeng, M. Xiao et al., “A deep learning algorithm using CT images to secreen for coronavirus disease (Covid-19)”, medRxiv, 2020. doi:10.1101/2020.02.14.20023028
  • [22] X. Wang, X. Deng, Q. Fu, Q. Zhou, J. Feng et al., “A weakly-supervised framework for Covid-19 classification and lesion localization from chest CT”, IEEE Trans Med Imaging, vol. 39, no. 8, pp. 2615-2625, 2020. doi:10.1109/TMI.2020.2995965
  • [23] X. Xu, X. Jiang, C. Ma, P. Du, X. Li et al., “Deep learning system to screen coronavirus disease 2019 pneumonia”, arXiv, 2002.09334, 2020. https://arxiv.org/abs/2002.09334
  • [24] S.H. Yoo, H. Geng, T.L. Chiu, S.K. Yu, D.C. Cho et al., “Deep learning-based decision-tree classifier for Covid-19 diagnosis from chest X-ray imaging”, Front Med, vol. 7, no. 427, pp. 1-8, 2020. doi: 10.3389/fmed.2020.00427
  • [25] S. Albahli, “A deep neural network to distinguish covid-19 from other chest diseases using X-ray images”, Curr Med Imaging Rev, vol. 16, pp. 1-11, 2020. doi: 10.2174/1573405616666200604163954
  • [26] J. Civit-Masot, F. Luna-Perejon, M.D. Morales, A. Civit, “Deep learning system for Covid-19 diagnosis aid using X-ray pulmonary images”, Appl Sci, vol. 10, no. 13, pp. 4060, 2020. doi:10.3390/app10134640
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  • [28] S. Ahuja, B.K. Panigrahi, N. Dey, V. Rajinikanth, T.K. Gandhi, “Deep transfer learning-based automated detection of Covid-19 from lung CT scan slices”, Appl Intell, pp. 1-15, 2020. doi:10.1007/s10489-020-01826-w
  • [29] E. Soares, P. Angelov, S. Biaso, M.H. Froes, D.K Abe, "SARS-CoV-2 CT Scan Dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification", MedRxiv, 2020.
  • [30] P. Silva, E. Luz, G. Silva, G. Moreira, R. Silva, D. Lucio, D. Menotti, "Covid-19 Detection in CT Images with Deep Learning: A Voting-Based Scheme And Cross-Datasets Anaşysis", Informatics in Medicine Unlocked, vol. 20, pp. 100427, 2020.
  • [31] S. Yazdani, S. Minaee, R. Kafieh, N. Saeedizadeh, M. Sonka, "Covid CT-Net: Predicting Covid-19 from Chest CT Images using Attentional Convolutional Network", arXiv, 2020.
  • [32] D. Konar, B.K. Panigrahi, S. Bhattacharyya, N. Dey, "Auto-Diagnosis of COVID-19 using lung CT images with semi-supervised shallow learning network”, IEEE Access, vol. 9, pp. 28716-28728, 2020. doi: 10.1109/ACCESS.2021.3058854.
  • [33] Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, “Backpropagation applied to handwritten zip code recognition”, Neural Comput, vol. 1, no. 4, pp. 541-551, 1989. doi: 10.1162/neco.1989.1.4.541
  • [34] I. Goodfellow, Y. Bengio, A. Courville, "Deep Learning", MIT Press, 2016.
  • [35] V. Nair, G.E. Hinton, "Rectified Linear Units Improve Restricted Boltzmann Machines", In Proc.: 27th International Conference on Machine Learning (ICML'10), June 21-24, Haifa, Israel pp. 807-814, 2010.
  • [36] Y. LeCun, L. Jackel, L. Bottou, C. Cortes, J.S. Denker, H. Drucker, I. Guyon, U. Muller, E. Sackinger, P. Simard et al., “Learning algorithms for classification: A comparison on handwritten digit recognition”, Neural Networks: The Statistical Mechanics Perspective, pp. 261-276, 1995.
  • [37] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, “Going deeper with convolutions”, In: Proceedings of the IEEE conference on computer vision and pattern recognition. arXiv:1409.4842, 2014. https://arxiv.org/abs/1409.4842
  • [38] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, “Rethinking the inception architecture for computer vision”, arXiv:1512.00567, 2015. https://arxiv.org/abs/1512.00567
  • [39] C. Szegedy, S. Ioffe, V. Vanhoucke, A. Alemi, “Inception-v4, Inception-ResNet and the impact of residual connections on learning”, arXiv:1602.07261, 2016. https://arxiv.org/abs/1602.07261
  • [40] F. Chollet, “Xception: Deep learning with depthwise separable convolutions”, arXiv, arXiv:1610.02357v3, 2017. https://arxiv.org/abs/1610.02357
  • [41] L. Sifre, “Rigid-motion scattering for image classification”, Ph.D. thesis, 2014.
  • [42] F. Chollet, “Keras”, 2015. https://github.com/fchollet/keras
  • [43] A. Martin, A. Agarwal, P. Barham, E. Brevdo, Z. Chen et al., "TensorFlow: Large-scale machine learning on heterogeneous systems" (software available from: tensorflow.org), 2015.
  • [44] M.D. Zeiler, “Adadelta: An adaptive learning rate method”, ArXiv abs/1212.5701, 2012. https://arxiv.org/abs/1212.5701v1
There are 44 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence, Engineering
Journal Section Research Articles
Authors

Özlem Polat 0000-0002-9395-4465

Publication Date June 30, 2021
Submission Date April 1, 2021
Acceptance Date May 5, 2021
Published in Issue Year 2021 Volume: 25 Issue: 3

Cite

APA Polat, Ö. (2021). Detection of Covid-19 from Chest CT Images using Xception Architecture: A Deep Transfer Learning based Approach. Sakarya University Journal of Science, 25(3), 800-810. https://doi.org/10.16984/saufenbilder.903886
AMA Polat Ö. Detection of Covid-19 from Chest CT Images using Xception Architecture: A Deep Transfer Learning based Approach. SAUJS. June 2021;25(3):800-810. doi:10.16984/saufenbilder.903886
Chicago Polat, Özlem. “Detection of Covid-19 from Chest CT Images Using Xception Architecture: A Deep Transfer Learning Based Approach”. Sakarya University Journal of Science 25, no. 3 (June 2021): 800-810. https://doi.org/10.16984/saufenbilder.903886.
EndNote Polat Ö (June 1, 2021) Detection of Covid-19 from Chest CT Images using Xception Architecture: A Deep Transfer Learning based Approach. Sakarya University Journal of Science 25 3 800–810.
IEEE Ö. Polat, “Detection of Covid-19 from Chest CT Images using Xception Architecture: A Deep Transfer Learning based Approach”, SAUJS, vol. 25, no. 3, pp. 800–810, 2021, doi: 10.16984/saufenbilder.903886.
ISNAD Polat, Özlem. “Detection of Covid-19 from Chest CT Images Using Xception Architecture: A Deep Transfer Learning Based Approach”. Sakarya University Journal of Science 25/3 (June 2021), 800-810. https://doi.org/10.16984/saufenbilder.903886.
JAMA Polat Ö. Detection of Covid-19 from Chest CT Images using Xception Architecture: A Deep Transfer Learning based Approach. SAUJS. 2021;25:800–810.
MLA Polat, Özlem. “Detection of Covid-19 from Chest CT Images Using Xception Architecture: A Deep Transfer Learning Based Approach”. Sakarya University Journal of Science, vol. 25, no. 3, 2021, pp. 800-1, doi:10.16984/saufenbilder.903886.
Vancouver Polat Ö. Detection of Covid-19 from Chest CT Images using Xception Architecture: A Deep Transfer Learning based Approach. SAUJS. 2021;25(3):800-1.

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