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• W3208904543 abstract "Severe acute respiratory syndrome coronavirus (SARS-CoV-2) also named COVID-19, aggressively spread all over the world in just a few months. Since then, it has multiple variants that are far more contagious than its parent. Rapid and accurate diagnosis of COVID-19 and its variants are crucial for its treatment, analysis of lungs damage and quarantine management. Deep learning-based solution for efficient and accurate diagnosis to COVID-19 and its variants using Chest X-rays, and computed tomography images could help to counter its outbreak. This work presents a novel depth-wise residual network with an atrous mechanism for accurate segmentation and lesion location of COVID-19 affected areas using volumetric CT images. The proposed framework consists of 3D depth-wise and 3D residual squeeze and excitation block in cascaded and parallel to capture uniformly multi-scale context (low-level detailed, mid-level comprehensive and high-level rich semantic features). The squeeze and excitation block adaptively recalibrates channel-wise feature responses by explicitly modeling inter-dependencies between various channels. We further have introduced an atrous mechanism with a different atrous rate as the bottom layer. Extensive experiments on benchmark CT datasets showed considerable gain (5%) for accurate segmentation and lesion location of COVID-19 affected areas." @default.
• W3208904543 created "2021-11-08" @default.
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• W3208904543 date "2021-10-26" @default.
• W3208904543 modified "2023-09-27" @default.
• W3208904543 title "Multilevel depth-wise context attention network with atrous mechanism for segmentation of COVID19 affected regions" @default.
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• W3208904543 doi "https://doi.org/10.1007/s00521-021-06636-w" @default.
• W3208904543 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8546198" @default.
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• W3208904543 hasPublicationYear "2021" @default.
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