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Article Dans Une Revue Sensors Année : 2021

Covid-19 recognition using ensemble-cnns in two new chest x-ray databases

Résumé

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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Dates et versions

hal-03542170 , version 1 (25-01-2022)

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Edoardo Vantaggiato, Emanuela Paladini, Fares Bougourzi, Cosimo Distante, Abdenour Hadid, et al.. Covid-19 recognition using ensemble-cnns in two new chest x-ray databases. Sensors, 2021, 21 (5), pp.1-20. ⟨10.3390/s21051742⟩. ⟨hal-03542170⟩
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