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Covid-19 recognition using ensemble-cnns in two new chest x-ray databases
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The used datasets were obtained from publically open source datastes from: 1 ieee8023/covid-chestxray-dataset https://github.com/ieee8023/covid-chestxray-dataset (accessed on 2 March 2021); 2 Chest X-Ray Images (Pneumonia) from Kaggle https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia (accessed on 2 March 2021); 3 RSNA Pneumonia Detection Challenge from Kaggle https://www.kaggle.com/c/rsna-pneumonia-detection-challenge (accessed on 2 March 2021); 4 A Large Chest X-Ray Dataset - CheXpert https://stanfordmlgroup.github.io/competitions/chexpert/ (accessed on 2 March 2021); 5 NLM-MontgomerySet https://lhncbc.nlm.nih.gov/LHC-publications/pubs/TuberculosisChestXrayImageDataSets.html (accessed on 2 March 2021); 6 NLM-ChinaCXRSet https://lhncbc.nlm.nih.gov/LHC-publications/pubs/TuberculosisChestXrayImageDataSets.html (accessed on 2 March 2021); 7 Algeria Hospital of Tolga https://github.com/Edo2610/Covid-19_X-ray_Two-proposed-Databases/tree/main/Datasets/5-classes/Test/Covid-19 (accessed on 2 March 2021).. International audience. 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.
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