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- W3023908178 abstract "Abstract Recent studies indicated that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 detection. In this work, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia infected area segmentation in CT images for the detection of COVID-19. We explore the efficacy of U-Nets and Fully Convolutional Neural Networks in this task using real-world CT data from COVID-19 patients. The results indicate that Fully Convolutional Neural Networks are capable of accurate segmentation despite the class imbalance on the dataset and the man-made annotation errors on the boundaries of symptom manifestation areas, and can be a promising method for further analysis of COVID-19 induced pneumonia symptoms in CT images. Impact Statement Fully Convolutional Neural Networks appear to be an accurate segmentation method in CT scans for COVID-19 pneumonia and could assist in the detection as a fast and cost-effective option." @default.
- W3023908178 created "2020-05-13" @default.
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- W3023908178 date "2020-05-11" @default.
- W3023908178 modified "2023-10-18" @default.
- W3023908178 title "Deep learning models for COVID-19 infected area segmentation in CT images" @default.
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- W3023908178 doi "https://doi.org/10.1101/2020.05.08.20094664" @default.
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