Skip to main content
SpringerLink
Log in

The Detection of COVID-19 in CT Medical Images: A Deep Learning Approach

  • Chapter
  • First Online:
Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach

Part of the book series: Studies in Big Data ((SBD,volume 78))

Abstract

The COVID-19 coronavirus is one of the latest viruses that hit the earth in the new century. It was declared as a pandemic by the World Health Organization in 2020. In this chapter, a model for the detection of COVID-19 virus from CT chest medical images will be presented. The proposed model is based on Generative Adversarial Networks (GAN), and a fine-tuned deep transfer learning model. GAN is used to generate more images from the available dataset. While deep transfer models are used to classify the COVID-19 virus from the normal class. The original dataset consists of 746 images. The is divided into two parts; 90% for the training and validation phase, while 10% for the testing phase. The 90% then is divided into 80% percent for the training and 20% percent for the validation after using GAN as image augmenter. The proposed GAN architecture raises the number of images in the training and validation phase to be 10 times larger than the original dataset. The deep transfer models which are selected for experimental trials are Resnet50, Shufflenet, and Mobilenet. They were selected as they include a medium number of layers on their architectures if they are com-pared with large deep transfer models such as DenseNet, and Inception-ResNet. This will reflect on the performance of the proposed model in terms of reducing training time, memory and CPU usage. The experimental trials show that Shufflenet is selected to be the optimal deep transfer learning in the proposed model as it achieves the highest possible for testing accuracy and performance metrics. Shufflenet achieves an overall testing accuracy with 84.9, and 85.33% in all performance metrics which include recall, precision, and F1 score.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Liu, J., et al.: Hydroxychloroquine, a less toxic derivative of chloroquine, is effective in inhibiting SARS-CoV-2 infection in vitro. Cell Discov. 6(1), 6–9 (2020). https://doi.org/10.1038/s41421-020-0156-0

    Article  Google Scholar 

  2. Hageman, J.R.: The coronavirus disease 2019 (COVID-19). Pediatr. Ann. (2020). https://doi.org/10.3928/19382359-20200219-01

  3. Singhal, T.: A review of coronavirus disease-2019 (COVID-19). Indian J. Pediatr. 87(4), 281–286 (2020). https://doi.org/10.1007/s12098-020-03263-6

    Article  Google Scholar 

  4. Huang, P., et al.: Use of chest CT in combination with negative RT-PCR assay for the 2019 novel coronavirus but high clinical suspicion. Radiology 295(1), 22–23 (2020). https://doi.org/10.1148/radiol.2020200330

    Article  Google Scholar 

  5. Cinkooglu, A., Bayraktaroglu, S., Savas, R.: Lung changes on chest CT during 2019 novel coronavirus (COVID-19) pneumonia. Eur. J. Breast Heal. 16(2), 89–90 (2020). https://doi.org/10.5152/ejbh.2020.010420

    Article  Google Scholar 

  6. Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L.H., Aerts, H.J.W.L.: Artificial intelligence in radiology. Nat. Rev. Cancer (2018). https://doi.org/10.1038/s41568-018-0016-5

    Article  Google Scholar 

  7. Iqbal, T., Ali, H.: Generative adversarial network for medical images (MI-GAN). J. Med. Syst. (2018). https://doi.org/10.1007/s10916-018-1072-9

    Article  Google Scholar 

  8. Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data (2019). https://doi.org/10.1186/s40537-019-0197-0

    Article  Google Scholar 

  9. Yi, X., Walia, E., Babyn, P.: Generative adversarial network in medical imaging: a review. Med. Image Anal. (2019). https://doi.org/10.1016/j.media.2019.101552

    Article  Google Scholar 

  10. Oakden-Rayner, L.: CheXNet: an in-depth review. Luke Oakden-Rayner Blog (2019)

    Google Scholar 

  11. Irvin, J., et al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison (2019)

    Google Scholar 

  12. Kermany, D., Zhang, K., Goldbaum, M.: Labeled optical coherence tomography (OCT) and chest X-ray images for classification (2018)

    Google Scholar 

  13. Saraiva A.A., et al.: Classification of images of childhood pneumonia using convolutional neural networks. In: BIOIMAGING 2019—6th International Conference on Bioimaging, Proceedings; Part 12th International Joint Conference on Biomedical Engineering Systems and Technologies BIOSTEC 2019, pp. 112–119 (2019). https://doi.org/10.5220/0007404301120119

  14. Liang G., Zheng, L.: A transfer learning method with deep residual network for pediatric pneumonia diagnosis. Comput. Methods Programs Biomed. (2020), https://doi.org/10.1016/j.cmpb.2019.06.023

  15. Wu, H., Xie, P., Zhang, H., Li, D., Cheng, M.: Predict pneumonia with chest X-ray images based on convolutional deep neural learning networks. J. Intell. Fuzzy Syst. (2020). https://doi.org/10.3233/jifs-191438

  16. Loey, M., Smarandache, F., Khalifa, N.E.M.: Within the lack of COVID-19 benchmark dataset : a novel GAN with deep transfer learning for corona-virus detection in chest X-ray images (2020)

    Google Scholar 

  17. Huang, L., et al.: Serial quantitative chest CT assessment of COVID-19: deep-learning approach. Radiol. Cardiothorac. Imaging 2(2), e200075 (2020). https://doi.org/10.1148/ryct.2020200075

    Article  Google Scholar 

  18. Jin S., et al.: AI-assisted CT imaging analysis for COVID-19 screening: building and deploying a medical AI system in four weeks. medRxiv p. 2020.03.19.20039354 (2020). https://doi.org/10.1101/2020.03.19.20039354

  19. Li L., et al.: Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology, p. 200905 (2020). https://doi.org/10.1148/radiol.2020200905

  20. ELGhamrawy S.M., Hassanien, A.E.: Diagnosis and prediction model for COVID19 patients response to treatment based on convolutional neural networks and whale optimization algorithm using CT images. medRxiv p. 2020.04.16.20063990 (2020). https://doi.org/10.1101/2020.04.16.20063990

  21. Sandfort, V., Yan, K., Pickhardt, P.J., Summers, R.M.: Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks. Sci. Rep. 9(1), 16884 (2019). https://doi.org/10.1038/s41598-019-52737-x

    Article  Google Scholar 

  22. Antoniou, A., Storkey, A., Edwards, H.: Data augmentation generative adversarial networks (2017) [Online]. Available: http://arxiv.org/abs/1711.04340

  23. Alqahtani, H., Kavakli-Thorne, M., Kumar, G.: Applications of generative adversarial networks (GANs): an updated review. Arch. Comput. Methods Eng. (2019). https://doi.org/10.1007/s11831-019-09388-y

    Article  Google Scholar 

  24. Goodfellow, I.J., et al.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. (2014). https://doi.org/10.3156/jsoft.29.5_177_2

  25. Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4), 427–437 (2009). https://doi.org/10.1016/j.ipm.2009.03.002

    Article  Google Scholar 

  26. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  27. Eraslan, G., Avsec, Ž., Gagneur, J., Theis, F.J.: Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20(7), 389–403 (2019). https://doi.org/10.1038/s41576-019-0122-6

    Article  Google Scholar 

  28. Voulodimos, A., Doulamis, N., Doulamis, A., Protopapadakis, E.: Deep learning for computer vision: a brief review. Comput. Intell. Neurosci. 2018, 7068349 (2018). https://doi.org/10.1155/2018/7068349

  29. Riordon, J., Sovilj, D., Sanner, S., Sinton, D., Young, E.W.K.: Deep learning with microfluidics for biotechnology. Trends Biotechnol. 37(3), 310–324 (2019). https://doi.org/10.1016/j.tibtech.2018.08.005

    Article  Google Scholar 

  30. You, J., McLeod, R.D., Hu, P.: Predicting drug-target interaction network using deep learning model. Comput. Biol. Chem. 80, 90–101 (2019). https://doi.org/10.1016/j.compbiolchem.2019.03.016

    Article  Google Scholar 

  31. Jaganathan, K., et al.: Predicting splicing from primary sequence with deep learning. Cell 176(3), 535–548.e24 (2019). https://doi.org/10.1016/j.cell.2018.12.015

    Article  Google Scholar 

  32. Cao, C., et al.: Deep learning and its applications in biomedicine. Genomics, Proteomics Bioinf. 16(1), 17–32 (2018). https://doi.org/10.1016/j.gpb.2017.07.003

    Article  MathSciNet  Google Scholar 

  33. Liu, S., Deng, W.: Very deep convolutional neural network based image classification using small training sample size. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 730–734 (2015) https://doi.org/10.1109/acpr.2015.7486599

  34. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015, pp. 1–9. https://doi.org/10.1109/cvpr.2015.7298594

  35. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. https://doi.org/10.1109/CVPR.2016.90

  36. Zhao, J. Zhang, Y., He, X., Xie, P.: COVID-CT-dataset: a CT scan dataset about COVID-19. pp. 1–5 (2020)

    Google Scholar 

  37. Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2261–2269. https://doi.org/10.1109/cvpr.2017.243

  38. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-ResNet and the impact of residual connections on learning In: 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (2017)

    Google Scholar 

  39. Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2018. https://doi.org/10.1109/cvpr.2018.00716

  40. Qin, Z., Zhang, Z., Chen, X., Wang, C., Peng, Y.: Fd-Mobilenet: improved Mobilenet with a fast downsampling strategy. In: Proceedings—International Conference on Image Processing, ICIP, 2018, https://doi.org/10.1109/icip.2018.8451355

  41. Khalifa, N.E.M., Taha, M.H.N., Hassanien, A.E., Hemedan, A.A.: Deep bacteria: robust deep learning data augmentation design for limited bacterial colony dataset. Int. J. Reason. Intell. Syst. 11(3), 256 (2019). https://doi.org/10.1504/IJRIS.2019.102610

    Article  Google Scholar 

  42. Khalifa, N.E.M., Taha, M.H.N., Ali, D.E., Slowik, A., Hassanien, A.E.: Artificial intelligence technique for gene expression by tumor RNA-Seq data: a novel optimized deep learning approach. IEEE Access (2020). https://doi.org/10.1109/access.2020.2970210

  43. Khalifa, N.E.M., Taha, M.H.N., Hassanien, A.E., Selim, I.M.: Deep Galaxy: classification of Galaxies based on deep convolutional neural networks (2017). arXiv:1709.02245

  44. Khalifa, N., Loey, M., Taha, M., Mohamed, H.: Deep transfer learning models for medical diabetic retinopathy detection. Acta Inform. Medica 27(5), 327 (2019). https://doi.org/10.5455/aim.2019.27.327-332

    Article  Google Scholar 

  45. Khalifa, N.E.M., Loey, M., Taha, M.H.N.: Insect pests recognition based on deep transfer learning models. J. Theor. Appl. Inf. Technol. 98(1), 60–68 (2020)

    Google Scholar 

  46. Žižka, J., Dařena, F., Svoboda, A., Žižka, J., Dařena, F., Svoboda, A.: Adaboost. In: Text Mining with Machine Learning (2019)

    Google Scholar 

  47. Goutte C., Gaussier, E.: A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: Losada D.E., Fernández-Luna J.M. (eds.) Advances in Information Retrieval, pp. 345–359. Berlin, Heidelberg: Springer Berlin Heidelberg (2005)

    Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge the support of NVIDIA Corporation, which donated the Titan X GPU used in this research.

Author information

Authors and Affiliations

  1. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Nour Eldeen M. Khalifa, Mohamed Hamed N. Taha & Aboul Ella Hassanien

  2. Forensic Medicine and Clinical Toxicology Department, Faculty of Medicine, Cairo University, Giza, Egypt

    Sarah Hamed N. Taha

  3. Scientific Research Group in Egypt (SRGE), Giza, Egypt

    Nour Eldeen M. Khalifa, Mohamed Hamed N. Taha & Aboul Ella Hassanien

Authors
  1. Nour Eldeen M. Khalifa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed Hamed N. Taha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aboul Ella Hassanien
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sarah Hamed N. Taha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nour Eldeen M. Khalifa .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul-Ella Hassanien

  2. Department of Computer Science and Engineering, JIS University, Kolkata, India

    Nilanjan Dey

  3. Department of Computer Engineering, MISR Higher Institute for Engineering and Technology, Mansoura, Egypt

    Sally Elghamrawy

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Khalifa, N.E.M., Taha, M.H.N., Hassanien, A.E., Taha, S.H.N. (2020). The Detection of COVID-19 in CT Medical Images: A Deep Learning Approach. In: Hassanien, AE., Dey, N., Elghamrawy, S. (eds) Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach. Studies in Big Data, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-55258-9_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-55258-9_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-55257-2

  • Online ISBN: 978-3-030-55258-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation