Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume 81, Issue 21, Pages 30615-30645Publisher
SPRINGER
DOI: 10.1007/s11042-022-12156-z
Keywords
COVID-19; Chest X-ray; Convolutional neural networks; CheXNet; Imaging features
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This study focuses on efficiently detecting imaging features of novel coronavirus pneumonia using deep convolutional neural networks. The proposed COVID-CXNet model is capable of precise localization based on relevant and meaningful features, which is a step towards a fully automated and robust COVID-19 detection system.
One of the primary clinical observations for screening the novel coronavirus is capturing a chest x-ray image. In most patients, a chest x-ray contains abnormalities, such as consolidation, resulting from COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from several sources are collected, and one of the largest publicly accessible datasets is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized to develop COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system.
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