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• W4310251675 abstract "The coronavirus (COVID-19) disease appeared as a respiratory system disorder and has triggered pneumonia outbreaks globally. As this COVID-19 disease drastically spread around the world, computed tomography (CT) has helped to diagnose it rapidly. It is imperative to implement a faultless computer-aided model for detecting COVID-19-affected patients through CT images. Therefore, a detail extraction pyramid network (DEPNet) is proposed to predict COVID-19-affected cases from CT images of the COVID-CT-MD dataset. In this study, the COVID-CT-MD dataset is applied to detect the accuracy of the deep learning technique; the dataset has CT scans of 169 patients; among those, 60 patients are COVID-19 positive patients, and 76 cases are normal. These affected patients were clinically verified with the standard hospital. The deep learning-oriented CT diagnosis model is implemented to detect COVID-19-affected patients. The experiment revealed that the proposed model categorized COVID-19 cases from other respiratory-oriented diseases with 99.45% accuracy. Further, this model selected the exact lesion parts, mainly ground-glass opacity, which helped the doctors to diagnose visually." @default.
• W4310251675 created "2022-11-30" @default.
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• W4310251675 date "2022-11-29" @default.
• W4310251675 modified "2023-10-16" @default.
• W4310251675 title "Intelligent diagnosis of coronavirus with computed tomography images using a deep learning model" @default.
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• W4310251675 doi "https://doi.org/10.1117/1.jei.32.2.021406" @default.
• W4310251675 hasPublicationYear "2022" @default.
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