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COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases

1
Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy
2
IEMN UMR CNRS 8520, Université Polytechnique Hauts de France, UPHF, 59300 Famars, France
3
Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy
*
Authors to whom correspondence should be addressed.
Academic Editor: Daniele Cocco
Sensors 2021, 21(5), 1742; https://doi.org/10.3390/s21051742
Received: 21 January 2021 / Revised: 14 February 2021 / Accepted: 23 February 2021 / Published: 3 March 2021
(This article belongs to the Section Sensing and Imaging)
The recognition of COVID-19 infection from X-ray images is an emerging field in the learning and computer vision community. Despite the great efforts that have been made in this field since the appearance of COVID-19 (2019), the field still suffers from two drawbacks. First, the number of available X-ray scans labeled as COVID-19-infected is relatively small. Second, all the works that have been carried out in the field are separate; there are no unified data, classes, and evaluation protocols. In this work, based on public and newly collected data, we propose two X-ray COVID-19 databases, which are three-class COVID-19 and five-class COVID-19 datasets. For both databases, we evaluate different deep learning architectures. Moreover, we propose an Ensemble-CNNs approach which outperforms the deep learning architectures and shows promising results in both databases. In other words, our proposed Ensemble-CNNs achieved a high performance in the recognition of COVID-19 infection, resulting in accuracies of 100% and 98.1% in the three-class and five-class scenarios, respectively. In addition, our approach achieved promising results in the overall recognition accuracy of 75.23% and 81.0% for the three-class and five-class scenarios, respectively. We make our databases of COVID-19 X-ray scans publicly available to encourage other researchers to use it as a benchmark for their studies and comparisons. View Full-Text
Keywords: COVID-19; deep learning; convolutional neural network; Ensemble-CNNs; X-ray scans COVID-19; deep learning; convolutional neural network; Ensemble-CNNs; X-ray scans
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MDPI and ACS Style

Vantaggiato, E.; Paladini, E.; Bougourzi, F.; Distante, C.; Hadid, A.; Taleb-Ahmed, A. COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases. Sensors 2021, 21, 1742. https://doi.org/10.3390/s21051742

AMA Style

Vantaggiato E, Paladini E, Bougourzi F, Distante C, Hadid A, Taleb-Ahmed A. COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases. Sensors. 2021; 21(5):1742. https://doi.org/10.3390/s21051742

Chicago/Turabian Style

Vantaggiato, Edoardo; Paladini, Emanuela; Bougourzi, Fares; Distante, Cosimo; Hadid, Abdenour; Taleb-Ahmed, Abdelmalik. 2021. "COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases" Sensors 21, no. 5: 1742. https://doi.org/10.3390/s21051742

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