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- W4308752076 abstract "Ghosal, Palash Kumar, Amish Kundu, Soumya Snigdha Srivastava, Utkarsh Prakash Datta, Ashis Sarma, vsThe coronavirus pandemic has caused one of the biggest global crises. With an inevitable need for fast screening of the disease, deep learning-based segmentation of Covid-19 infected lung regions in computed tomography (CT) scans gained significant attention. The automated screening procedure generated results significantly faster than the manual screening techniques and directly helped provide a wider outreach to patients. Therefore, to aid in computer-aided diagnoses, this paper presents AUTCD-Net (AUTomated framework for efficient Covid-19 Diagnosis-Network), based on hierarchical resolution steps, to efficiently segment Covid-19 infected lung regions in CT scans. The approach results in a 0.71 dice score and rivals all previous state-of-the-art approaches. The overall evaluation combined with our in-depth model analysis, and critical inferences can be further extended for developing a computer-aided diagnostic (CAD) tool to assist the CT image reading process for detecting Covid-19 infected regions in the near future." @default.
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- W4308752076 date "2022-11-10" @default.
- W4308752076 modified "2023-09-28" @default.
- W4308752076 title "AUTCD-Net: An Automated Framework for Efficient Covid-19 Diagnosis on Computed Tomography Scans" @default.
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- W4308752076 doi "https://doi.org/10.1007/978-981-19-5090-2_10" @default.
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