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

Trivizakis,Eleftherios; Tsiknakis,Nikos; Vassalou,Evangelia E.; Papadakis,Georgios Z.; Spandidos,Demetrios A.; Sarigiannis,Dimosthenis; Tsatsakis,Aristidis; Papanikolaou,Nikolaos; Karantanas,Apostolos H.; Marias,Kostas

doi:10.3892/etm.2020.9210

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
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Affiliations: Computational Biomedicine Laboratory (CBML), Foundation for Research and Technology Hellas (FORTH), 70013 Heraklion, Greece, Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, Greece, Laboratory of Clinical Virology, Medical School, University of Crete, 71003 Heraklion, Greece, HERACLES Research Center on the Exposome and Health, Centre for Interdisciplinary Research and Innovation, Aristotle University of Thessaloniki, 57001 Thermi, Greece, Department of Forensic Sciences and Toxicology, Medical School, University of Crete, 71003 Heraklion, Greece
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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