Skip to main content

Fusing Deep Learning with Support Vector Machines to Detect COVID-19 in X-Ray Images

  • Conference paper
  • First Online:
Recent Challenges in Intelligent Information and Database Systems (ACIIDS 2022)

Abstract

Deep neural networks are powerful learning machines that have laid foundations for most of the recent advancements in data analysis. Their most important advantage lies in learning how to extract the features from raw data, and these deep features are later classified with fully-connected layers. Although there exist more effective classifiers, including support vector machines, their high computational complexity is a serious obstacle in using them for classifying highly-dimensional and often huge datasets of deep features. We introduce a new framework which allows us to classify the deep features with evolutionarily-optimized support vector machines and we apply it to a real-life problem of detecting COVID-19 from X-ray images. We demonstrate that the proposed approach is highly effective and it outperforms well-established transfer learning strategies, thus improving the potential of existing pre-trained deep models. It can be particularly beneficial in cases when the amount and quality of labeled data is insufficient for performing full training of a network, but still too large for training a regular support vector machine.

This work was supported by the National Science Centre under Grant DEC-2017/25/B/ST6/00474.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Aladeemy, M., Tutun, S., Khasawneh, M.T.: A new hybrid approach for feature selection and support vector machine model selection based on self-adaptive cohort intelligence. Expert Syst. Appl. 88, 118–131 (2017)

    Article  Google Scholar 

  2. Amancio, D.R., et al.: A systematic comparison of supervised classifiers. PLoS ONE 9(4), e94137 (2014)

    Article  Google Scholar 

  3. Anaby-Tavor, A., et al.: Do not have enough data? Deep learning to the rescue! In: Proceedings of AAAI Conference on Artificial Intelligence, vol. 34, pp. 7383–7390 (2020)

    Google Scholar 

  4. Boccaletti, S., Ditto, W., Mindlin, G., Atangana, A.: Modeling and forecasting of epidemic spreading: the case of COVID-19 and beyond. Chaos, Solitons Fractals 135, 109794 (2020)

    Article  Google Scholar 

  5. Bolhasani, H., Mohseni, M., Rahmani, A.M.: Deep learning applications for IoT in healthcare: a systematic review. Inform. Med. Unlock. 23, 100550 (2021)

    Article  Google Scholar 

  6. Borakati, A., Perera, A., Johnson, J., Sood, T.: Diagnostic accuracy of X-ray versus CT in COVID-19: a propensity-matched database study. BMJ Open. 10(11), e042946 (2020)

    Article  Google Scholar 

  7. Bosowski, P., Bosowska, J., Nalepa, J.: Evolving deep ensembles for detecting COVID-19 In Chest X-Rays. In: Proceedings of IEEE ICIP, pp. 3772–3776 (2021)

    Google Scholar 

  8. Bustos, A., Pertusa, A., Salinas, J.M., de la Iglesia-Vayá, M.: PadChest: a large chest x-ray image dataset with multi-label annotated reports. Med. Image Anal. 66, 101797 (2020)

    Article  Google Scholar 

  9. Chandra, T.B., Verma, K., Singh, B.K., Jain, D., Netam, S.S.: Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble. Expert Syst. Appl. 165, 113909 (2021)

    Article  Google Scholar 

  10. Chicco, D., Jurman, G.: The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom. 21(1), 6 (2020)

    Article  Google Scholar 

  11. Chowdhury, M.E.H., et al.: Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8, 132665–132676 (2020)

    Article  Google Scholar 

  12. Cohen, J.P., Morrison, P., Dao, L., Roth, K., Duong, T.Q., Ghassemi, M.: COVID-19 Image Data Collection: Prospective Predictions Are the Future. arXiv 2006.11988 (2020). https://github.com/ieee8023/covid-chestxray-dataset

  13. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    Article  MATH  Google Scholar 

  14. Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: Proceedings of IEEE CVPR, pp. 248–255 (2009)

    Google Scholar 

  15. Desai, S., et al.: Chest imaging representing a COVID-19 positive rural U.S. population. Sci. Data. 7(1), 414 (2020)

    Google Scholar 

  16. Dudzik, W., Kawulok, M., Nalepa, J.: Optimizing training data and hyperparameters of support vector machines using a memetic algorithm. In: Proceeding of ICMMI, pp. 229–238 (2019)

    Google Scholar 

  17. Dudzik, W., Nalepa, J., Kawulok, M.: Evolving data-adaptive support vector machines for binary classification. Knowl. Based Syst. 227, 107221 (2021)

    Article  Google Scholar 

  18. Girshick, R.: Fast R-CNN. In: Proceedings of IEEE ICCV, pp. 1440–1448 (2015)

    Google Scholar 

  19. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of IEEE CVPR, pp. 580–587 (2014)

    Google Scholar 

  20. He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition. arXiv e-prints arXiv:1512.03385, December 2015

  21. Huh, M., Agrawal, P., Efros, A.A.: What makes ImageNet good for transfer learning? arXiv preprint arXiv:1608.08614 (2016)

  22. Iglesia la de Vayá, M., et al.: BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients. arXiv e-prints arXiv:2006.01174 (2020)

  23. Irvin, J., et al.: CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. arXiv e-prints arXiv:1901.07031, January 2019

  24. Kawulok, M., Nalepa, J.: Towards robust SVM training from weakly labeled large data sets. In: Proceedings of IAPR ACPR, pp. 464–468 (2015)

    Google Scholar 

  25. Kawulok, M., Nalepa, J.: Dynamically adaptive genetic algorithm to select training data for SVMs. In: Bazzan, A.L.C., Pichara, K. (eds.) IBERAMIA 2014. LNCS (LNAI), vol. 8864, pp. 242–254. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12027-0_20

    Chapter  Google Scholar 

  26. Kiran, B.R., Thomas, D.M., Parakkal, R.: An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos. J. Imaging 4(2), 36 (2018)

    Article  Google Scholar 

  27. Le, Q.V., Sarlós, T., Smola, A.J.: FastFood: approximate kernel expansions in loglinear time. CoRR abs/1408.3060, pp. 1–8 (2014)

    Google Scholar 

  28. Melin, P., Monica, J.C., Sanchez, D., Castillo, O.: Multiple ensemble neural network models with fuzzy response aggregation for predicting COVID-19 time series: the case of Mexico. Healthcare 8(2), 181 (2020)

    Article  Google Scholar 

  29. Nalepa, J., Kawulok, M.: Selecting training sets for support vector machines: a review. Artif. Intell. Rev. 52(2), 857–900 (2019)

    Article  Google Scholar 

  30. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009)

    Article  Google Scholar 

  31. Ravi, A., Venugopal, H., Paul, S., Tizhoosh, H.R.: A dataset and preliminary results for umpire pose detection using SVM classification of deep features. In: Proceedings of IEEE SSCI, pp. 1396–1402. IEEE (2018)

    Google Scholar 

  32. Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv e-prints arXiv:1409.1556, September 2014

  33. Solorio-Fernández, S., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F.: A review of unsupervised feature selection methods. Artif. Intell. Rev. 53(2), 907–948 (2020)

    Article  Google Scholar 

  34. Sun, T., Wang, Y.: Modeling COVID-19 epidemic in Heilongjiang province, China. Chaos, Solitons Fractals 138, 109949 (2020)

    Article  Google Scholar 

  35. Tang, Y.: Deep learning using linear support vector machines. In: Proceedings of Workshop on Challenges in Representation Learning, ICML 2013 (2013)

    Google Scholar 

  36. Tulczyjew, L., Kawulok, M., Nalepa, J.: Unsupervised feature learning using recurrent neural nets for segmenting hyperspectral images. IEEE Geosci. Remote Sens. Lett. 18(12), 2142–2146 (2021)

    Article  Google Scholar 

  37. Varela-Santos, S., Melin, P.: A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks. Inf. Sci. 545, 403–414 (2021)

    Article  MathSciNet  Google Scholar 

  38. Wang, D., Mo, J., Zhou, G., Xu, L., Liu, Y.: An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images. PLoS ONE 15(11), 1–15 (2020)

    Article  Google Scholar 

  39. Wang, L., Wong, A., Lin, Z.Q., McInnis, P., Chung, A., Gunraj, H.: Actualmed-COVID-chestxray-dataset. https://github.com/agchung

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jakub Nalepa .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nalepa, J., Bosowski, P., Dudzik, W., Kawulok, M. (2022). Fusing Deep Learning with Support Vector Machines to Detect COVID-19 in X-Ray Images. In: Szczerbicki, E., Wojtkiewicz, K., Nguyen, S.V., Pietranik, M., Krótkiewicz, M. (eds) Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2022. Communications in Computer and Information Science, vol 1716. Springer, Singapore. https://doi.org/10.1007/978-981-19-8234-7_27

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-8234-7_27

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8233-0

  • Online ISBN: 978-981-19-8234-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics