Open Access

Advancing Covid‑19 differentiation with a robust preprocessing and integration of multi‑institutional open‑repository computer tomography datasets for deep learning analysis

  • Authors:
    • Eleftherios Trivizakis
    • Nikos Tsiknakis
    • Evangelia E. Vassalou
    • Georgios Z. Papadakis
    • Demetrios A. Spandidos
    • Dimosthenis Sarigiannis
    • Aristidis Tsatsakis
    • Nikolaos Papanikolaou
    • Apostolos H. Karantanas
    • Kostas Marias
  • View Affiliations

  • Published online on: September 11, 2020     https://doi.org/10.3892/etm.2020.9210
  • Article Number: 78
  • Copyright: © Trivizakis et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The coronavirus pandemic and its unprecedented consequences globally has spurred the interest of the artificial intelligence research community. A plethora of published studies have investigated the role of imaging such as chest X‑rays and computer tomography in coronavirus disease 2019 (COVID‑19) automated diagnosis. Οpen repositories of medical imaging data can play a significant role by promoting cooperation among institutes in a world‑wide scale. However, they may induce limitations related to variable data quality and intrinsic differences due to the wide variety of scanner vendors and imaging parameters. In this study, a state‑of‑the‑art custom U‑Net model is presented with a dice similarity coefficient performance of 99.6% along with a transfer learning VGG‑19 based model for COVID‑19 versus pneumonia differentiation exhibiting an area under curve of 96.1%. The above was significantly improved over the baseline model trained with no segmentation in selected tomographic slices of the same dataset. The presented study highlights the importance of a robust preprocessing protocol for image analysis within a heterogeneous imaging dataset and assesses the potential diagnostic value of the presented COVID‑19 model by comparing its performance to the state of the art.
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November-2020
Volume 20 Issue 5

Print ISSN: 1792-0981
Online ISSN:1792-1015

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Spandidos Publications style
Trivizakis E, Tsiknakis N, Vassalou EE, Papadakis GZ, Spandidos DA, Sarigiannis D, Tsatsakis A, Papanikolaou N, Karantanas AH, Marias K, Marias K, et al: Advancing Covid‑19 differentiation with a robust preprocessing and integration of multi‑institutional open‑repository computer tomography datasets for deep learning analysis. Exp Ther Med 20: 78, 2020.
APA
Trivizakis, E., Tsiknakis, N., Vassalou, E.E., Papadakis, G.Z., Spandidos, D.A., Sarigiannis, D. ... Marias, K. (2020). Advancing Covid‑19 differentiation with a robust preprocessing and integration of multi‑institutional open‑repository computer tomography datasets for deep learning analysis. Experimental and Therapeutic Medicine, 20, 78. https://doi.org/10.3892/etm.2020.9210
MLA
Trivizakis, E., Tsiknakis, N., Vassalou, E. E., Papadakis, G. Z., Spandidos, D. A., Sarigiannis, D., Tsatsakis, A., Papanikolaou, N., Karantanas, A. H., Marias, K."Advancing Covid‑19 differentiation with a robust preprocessing and integration of multi‑institutional open‑repository computer tomography datasets for deep learning analysis". Experimental and Therapeutic Medicine 20.5 (2020): 78.
Chicago
Trivizakis, E., Tsiknakis, N., Vassalou, E. E., Papadakis, G. Z., Spandidos, D. A., Sarigiannis, D., Tsatsakis, A., Papanikolaou, N., Karantanas, A. H., Marias, K."Advancing Covid‑19 differentiation with a robust preprocessing and integration of multi‑institutional open‑repository computer tomography datasets for deep learning analysis". Experimental and Therapeutic Medicine 20, no. 5 (2020): 78. https://doi.org/10.3892/etm.2020.9210