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      COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet.

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          Abstract

          Currently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lung images of such patients. Two structurally-different deep learning techniques, SegNet and U-NET, are investigated for semantically segmenting infected tissue regions in CT lung images.

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          Author and article information

          Journal
          BMC Med Imaging
          BMC medical imaging
          Springer Science and Business Media LLC
          1471-2342
          1471-2342
          Feb 09 2021
          : 21
          : 1
          Affiliations
          [1 ] Mechatronics Program for the Distinguished, Tishreen University, Distinction and Creativity Agency, Latakia, Syria.
          [2 ] Mechatronics Program for the Distinguished, Tishreen University, Distinction and Creativity Agency, Latakia, Syria. iyad.hatem@tishreen.edu.sy.
          Article
          10.1186/s12880-020-00529-5
          10.1186/s12880-020-00529-5
          7870362
          33557772
          578b7057-ffc3-4399-a736-e81c6ba41dc6
          History

          COVID-19,U-NET,Semantic segmentation,SegNet,Pneumonia,Computerized tomography

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