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• W3206409898 abstract "Early and automatic segmentation of lung infections from computed tomography images of COVID-19 patients is crucial for timely quarantine and effective treatment. However, automating the segmentation of lung infection from CT slices is challenging due to a lack of contrast between the normal and infected tissues. A CNN and GAN-based framework are presented to classify and then segment the lung infections automatically from COVID-19 lung CT slices. In this work, the authors propose a novel method named P2P-COVID-SEG to automatically classify COVID-19 and normal CT images and then segment COVID-19 lung infections from CT images using GAN. The proposed model outperformed the existing classification models with an accuracy of 98.10%. The segmentation results outperformed existing methods and achieved infection segmentation with accurate boundaries. The Dice coefficient achieved using GAN segmentation is 81.11%. The segmentation results demonstrate that the proposed model outperforms the existing models and achieves state-of-the-art performance." @default.
• W3206409898 created "2021-10-25" @default.
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• W3206409898 date "2021-10-01" @default.
• W3206409898 modified "2023-09-23" @default.
• W3206409898 title "P2P-COVID-GAN" @default.
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• W3206409898 doi "https://doi.org/10.4018/ijdwm.2021100105" @default.
• W3206409898 hasPublicationYear "2021" @default.
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