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

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-03, Vol.21 (5), p.1742-20
Main Authors: Vantaggiato, Edoardo, Paladini, Emanuela, Bougourzi, Fares, Distante, Cosimo, Hadid, Abdenour, Taleb-Ahmed, Abdelmalik
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computer Science
convolutional neural network
COVID-19
COVID-19 - diagnostic imaging
Deep Learning
Electronics
Engineering Sciences
Ensemble-CNNs
Humans
Networking and Internet Architecture
Neural Networks, Computer
Radiography, Thoracic
Signal and Image processing
X-ray scans
X-Rays
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21051742