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Explainable Deep Ensemble to Diagnose COVID-19 from CT Scans

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  • First Online:
Fuzzy Logic and Technology, and Aggregation Operators (EUSFLAT 2023, AGOP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14069))

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Abstract

Research on identification methods for the Coronavirus Disease 2019 (COVID-19) has increased in the last years and the need for automated detection methods has surged as well. Computed Tomography scan images have demonstrated to contain useful and sufficient information to detect COVID-19 by using machine learning and computational intelligence techniques. However, in order to expand their adoption in medical clinics, COVID-19 detection approaches need to drive the experts in the overall comprehension of the classification, to check the validity and meaningfulness of the prediction results. Herein, we propose a deep learning approach based on an ensemble of convolutional neural networks with the aim of detecting, very accurately and in an explainable way, COVID-19 patients by leveraging CT scan images. We also take advantage of transfer learning and apply the aforementioned deep ensemble to a large publicly available dataset, by clustering the images per lung lobe. Our results show good classification performance, good generalization potentials, as well as quite interpretable outcomes.

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Notes

  1. 1.

    https://doi.org/10.17632/8h65ywD2jr.3.

  2. 2.

    https://github.com/abdkhanstd/COVID-19.

  3. 3.

    https://www.tensorflow.org/.

  4. 4.

    https://bit.ly/34QJUSd.

  5. 5.

    https://keras.io/.

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Acknowledgments

The authors are grateful to Dr. Gaetana Cremone, Department of Radiology, “G. Fucito” Hospital, Mercato San Severino, SA, Italy, for her contribution in the evaluation of the explainability results. Prof. Riccardo Pecori is a member of the INdAM GNCS research group.

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Authors and Affiliations

  1. Department of Engineering, University of Sannio, 82100, Benevento, Italy

    Lerina Aversano, Mario Luca Bernardi & Chiara Verdone

  2. Department of Law and Digital Society, UnitelmaSapienza University, Rome, Italy

    Marta Cimitile

  3. SMARTEST Research Centre, eCampus University, 22060, Novedrate, CO, Italy

    Riccardo Pecori

  4. IMEM-CNR, Parco Area delle Scienze 37/A, 43124, Parma, Italy

    Riccardo Pecori

Authors
  1. Lerina Aversano
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  2. Mario Luca Bernardi
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  3. Marta Cimitile
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  4. Riccardo Pecori
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  5. Chiara Verdone
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Corresponding author

Correspondence to Riccardo Pecori .

Editor information

Editors and Affiliations

  1. University of the Balearic Islands, Palma, Spain

    Sebastia Massanet

  2. University of Oviedo, Gijón, Spain

    Susana Montes

  3. University of the Balearic Islands, Palma, Spain

    Daniel Ruiz-Aguilera

  4. University of the Balearic Islands, Palma, Spain

    Manuel González-Hidalgo

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Aversano, L., Bernardi, M.L., Cimitile, M., Pecori, R., Verdone, C. (2023). Explainable Deep Ensemble to Diagnose COVID-19 from CT Scans. In: Massanet, S., Montes, S., Ruiz-Aguilera, D., González-Hidalgo, M. (eds) Fuzzy Logic and Technology, and Aggregation Operators. EUSFLAT AGOP 2023 2023. Lecture Notes in Computer Science, vol 14069. Springer, Cham. https://doi.org/10.1007/978-3-031-39965-7_53

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  • DOI: https://doi.org/10.1007/978-3-031-39965-7_53

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-39965-7

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