Skip to main content
SpringerLink
Log in
Book cover

International Conference on Image Analysis and Processing

ICIAP 2022: Image Analysis and Processing – ICIAP 2022 pp 197–209Cite as

Optimized Fusion of CNNs to Diagnose Pulmonary Diseases on Chest X-Rays

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13231))

Abstract

Since the beginning of the COVID-19 pandemic, more than 350 million cases and 5 million deaths have occurred. Since day one, multiple methods have been provided to diagnose patients who have been infected. Alongside the gold standard of laboratory analyses, deep learning algorithms on chest X-rays (CXR) have been developed to support the COVID-19 diagnosis. The literature reports that convolutional neural networks (CNNs) have obtained excellent results on image datasets when the tests are performed in cross-validation, but such models fail to generalize to unseen data. To overcome this limitation, we exploit the strength of multiple CNNs by building an ensemble of classifiers via an optimized late fusion approach. To demonstrate the system’s robustness, we present different experiments on open source CXR datasets to simulate a real-world scenario, where scans of patients affected by various lung pathologies and coming from external datasets are tested. Promising performances are obtained both in cross-validation and in external validation, obtaining an average accuracy of 93.02% and 91.02%, respectively.

This is a preview of subscription content, 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   89.00
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

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Worldmeters coronavirus. https://www.worldometers.info/coronavirus/. Accessed 01 Feb 2022

  2. Aljondi, R., et al.: Diagnostic value of imaging modalities for COVID-19: scoping review. J. Med. Internet Res. 22(8), e19673 (2020)

    Article  Google Scholar 

  3. Basu, S., et al.: Deep learning for screening COVID-19 using chest x-ray images. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 2521–2527. IEEE (2020)

    Google Scholar 

  4. Brown, G., et al.: Diversity creation methods: a survey and categorisation. Inf. Fusion 6(1), 5–20 (2005)

    Google Scholar 

  5. Brunese, L., et al.: Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from x-rays. Comput. Methods Programs Biomed. 196, 105608 (2020)

    Google Scholar 

  6. Cavalcanti, G.D., et al.: Combining diversity measures for ensemble pruning. Pattern Recogn. Lett. 74, 38–45 (2016)

    Google Scholar 

  7. Cipollari, S., et al.: Convolutional neural networks for automated classification of prostate multiparametric magnetic resonance imaging based on image quality. J. Magn. Reson. Imaging 55(2), 480–490 (2022)

    Google Scholar 

  8. Cohen, J.P., et al.: COVID-19 image data collection: prospective predictions are the future. arXiv preprint arXiv:2006.11988 (2020)

  9. Das, D., Santosh, K.C., Pal, U.: Truncated inception net: COVID-19 outbreak screening using chest x-rays. Phys. Eng. Sci. Med. 43(3), 915–925 (2020). https://doi.org/10.1007/s13246-020-00888-x

  10. Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

    Google Scholar 

  11. Futoma, J., et al.: The myth of generalisability in clinical research and machine learning in health care. Lancet Digit. Health 2(9), e489–e492 (2020)

    Google Scholar 

  12. Guarrasi, V., et al.: A multi-expert system to detect COVID-19 cases in x-ray images. In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), pp. 395–400. IEEE (2021)

    Google Scholar 

  13. Guarrasi, V., et al.: Pareto optimization of deep networks for COVID-19 diagnosis from chest x-rays. Pattern Recogn. 121, 108242 (2022)

    Google Scholar 

  14. Hansen, L.K., et al.: Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (1990)

    Google Scholar 

  15. He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  16. Huang, G., et al.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  17. Iandola, F.N., et al.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \(<\)0.5MB model size. arXiv preprint arXiv:1602.07360 (2016)

  18. Jaeger, S., et al.: Two public chest x-ray datasets for computer-aided screening of pulmonary diseases. Quant. Imaging Med. Surg. 4(6), 475 (2014)

    Google Scholar 

  19. Krizhevsky, A.: One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997 (2014)

  20. Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Google Scholar 

  21. Pereira, R.M., et al.: COVID-19 identification in chest x-ray images on flat and hierarchical classification scenarios. Comput. Methods Programs Biomed. 194, 105532 (2020)

    Google Scholar 

  22. Pouyanfar, S., et al.: A survey on deep learning: algorithms, techniques, and applications. ACM Comput. Surv. (CSUR) 51(5), 1–36 (2018)

    Google Scholar 

  23. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

  24. Sandler, M., et al.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

    Google Scholar 

  25. Shiraishi, J., et al.: Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules. Am. J. Roentgenol. 174(1), 71–74 (2000)

    Google Scholar 

  26. Simonyan, K., et al.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  27. Soda, P., et al.: AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-x-rays. An Italian multicentre study. Med. Image Anal. 74, 102216 (2021)

    Google Scholar 

  28. Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

    Google Scholar 

  29. Wang, L., et al.: Covid-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest x-ray images. Sci. Rep. 10(1), 1–12 (2020)

    Google Scholar 

  30. Wang, X., et al.: ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097–2106 (2017)

    Google Scholar 

  31. Wynants, L., et al.: Prediction models for diagnosis and prognosis of COVID-19 infection: systematic review and critical appraisal. Brit. Med. J. 369 (2020)

    Google Scholar 

  32. Xie, S., et al.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)

    Google Scholar 

  33. Zagoruyko, S., et al.: Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)

Download references

Acknowledgements

This work is partially funded by: POR CAMPANIA FESR 2014–2020, AP1-OS1.3 project “Protocolli TC del torace a bassissima dose e tecniche di intelligenza artificiale per la diagnosi precoce e quantificazione della malattia da COVID-19” CUP D54I20001410002; EU project “University-Industrial Educational Centre in Advanced Biomedical and Medical Informatics (CeBMI) No. 612462-EPP-1-2019-1-SK-EPPKA2-KA”; “AI against COVID-19 Competition”, organized by IEEE SIGHT Montreal, Vision and Image Processing Research Group of the University of Waterloo, and DarwinAI Corp., and sponsored by Microsoft.

Author information

Authors and Affiliations

  1. Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Rome, Italy

    Valerio Guarrasi & Paolo Soda

  2. Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, Italy

    Valerio Guarrasi

Authors
  1. Valerio Guarrasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paolo Soda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Valerio Guarrasi .

Editor information

Editors and Affiliations

  1. Boston University, Boston, MA, USA

    Stan Sclaroff

  2. National Research Council, Lecce, Italy

    Cosimo Distante

  3. National Research Council, Lecce, Italy

    Marco Leo

  4. University of Catania, Catania, Italy

    Giovanni M. Farinella

  5. Technische Universität München, Garching, Germany

    Federico Tombari

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Guarrasi, V., Soda, P. (2022). Optimized Fusion of CNNs to Diagnose Pulmonary Diseases on Chest X-Rays. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13231. Springer, Cham. https://doi.org/10.1007/978-3-031-06427-2_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06427-2_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06426-5

  • Online ISBN: 978-3-031-06427-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation