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• W4317382452 abstract "In this paper, we present an approach for COVID-19 identification from chest X-ray images by using high-resolution neural networks. These networks allow to connect high-to-low convolution streams in parallel. They can maintain high-resolution representations and generate different resolutions throughout the whole process. The high-resolution based models have shown the superior performance in several applications. The experiments were evaluated on a collection of three data sources containing 24,786 lung X-ray images, which were categorized into three classes including covid pneumonia, non-pneumonia, and viral pneumonia. The proposed approach can attain the overall accuracy of 98.2% and 97.56% for the training and testing set, respectively. The accuracy for each class is 99.37%, 94.83%, and 97.27%, respectively, for non-pneumonia, covid-pneumonia, and viral-pneumonia." @default.
• W4317382452 created "2023-01-19" @default.
• W4317382452 creator A5005333711 @default.
• W4317382452 creator A5048415094 @default.
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• W4317382452 date "2022-12-20" @default.
• W4317382452 modified "2023-09-29" @default.
• W4317382452 title "An Approach for COVID-19 Identification from Chest X-ray Images Using High-Resolution Networks" @default.
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• W4317382452 doi "https://doi.org/10.1109/rivf55975.2022.10013838" @default.
• W4317382452 hasPublicationYear "2022" @default.
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