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Deep Efficient Neural Networks for Explainable COVID-19 Detection on CXR Images

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Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices (IEA/AIE 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12798))

Abstract

With the spread of COVID-19 pandemic worldwide, medical imaging modalities and deep learning can play an important role in the fight against this disease. Recent years have seen the impressive results obtained using deep neural networks in different fields. Radiology is among the medical fields that can benefit from this recent progress and improve disease’s diagnosis, monitoring and prognosis. In this work, we propose the use of a deep efficient neural network based on EfficientNet B7 to detect COVID-19 in Chest X-rays (CXR). The obtained results on a large dataset are promising and show the high performance of the proposed model, with in average an accuracy of 95%, an AUC of 95%, a specificity of 90% and a sensitivity of 97%. In addition, an explainability model was developed and shows the high performance and high generalization degree of the proposed model in detecting and highlighting the signs of the disease.

This work was supported by Atlantic Canada Opportunities Agency (ACOA), Regional Economic Growth through Innovation - Business Scale-Up and Productivity (Project 217148), Natural Sciences and Engineering Research Council of Canada (NSERC), Alliance Grants (ALLRP 552039-20), New Brunswick Innovation Foundation (NBIF), COVID-19 Research Fund (COV2020-042), and the Microsoft AI For Health program.

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Acknowledgments

The authors would like to acknowledge El Mostafa Bouattane and Joseph Abdulnour (Institut du Savoir Montfort, Hôpital Montfort) for their assistance with organizing and sharing Montfort anonymized CXR images.

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Authors and Affiliations

  1. Perception, Robotics, and Intelligent Machines Research Group (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB, E1A 3E9, Canada

    Mohamed Chetoui & Moulay A. Akhloufi

Authors
  1. Mohamed Chetoui
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  2. Moulay A. Akhloufi
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Corresponding author

Correspondence to Moulay A. Akhloufi .

Editor information

Editors and Affiliations

  1. i-SOMET Incorporate Association, Morioka, Japan

    Hamido Fujita

  2. Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Ali Selamat

  3. Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  4. Texas State University San Marcos, San Marcos, TX, USA

    Moonis Ali

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The authors declare no conflict of interest.

Ethical Conduct of Research

The research ethics board at Université de Moncton waived ethics approval since our study does not involve direct work with humans, but only with anonymized images as described in the dataset section.

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Chetoui, M., Akhloufi, M.A. (2021). Deep Efficient Neural Networks for Explainable COVID-19 Detection on CXR Images. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12798. Springer, Cham. https://doi.org/10.1007/978-3-030-79457-6_29

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  • DOI: https://doi.org/10.1007/978-3-030-79457-6_29

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-79457-6

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