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10.7717/peerj-cs.368 http://scihub22266oqcxt.onion/10.7717/peerj-cs.368 33817018!7959645!33817018     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33817018&cmd=llinks): Failed to open stream: HTTP request failed! 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Performance analysis of lightweight CNN models to segment infectious lung tissues of COVID-19 cases from tomographic images |pdf=|usr=}}{{33817018}} gab.com Text Performance analysis of lightweight CNN models to segment infectious lung tissues of COVID-19 cases from tomographic images JO: PeerJ Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=33817018 #FOAvip The pandemic of Coronavirus Disease-19 (COVID-19) has spread around the world, causing an existential health crisis. Automated detection of COVID-19 infections in the lungs from Computed Tomography (CT) images offers huge potential in tackling the problem of slow detection and augments the conventional diagnostic procedures. However, segmenting COVID-19 from CT Scans is problematic, due to high variations in the types of infections and low contrast between healthy and infected tissues. While segmenting Lung CT Scans for COVID-19, fast and accurate results are required and furthermore, due to the pandemic, most of the research community has opted for various cloud based servers such as Google Colab, etc. to develop their algorithms. High accuracy can be achieved using Deep Networks but the prediction time would vary as the resources are shared amongst many thus requiring the need to compare different lightweight segmentation model. To address this issue, we aim to analyze the segmentation of COVID-19 using four Convolutional Neural Networks (CNN). The images in our dataset are preprocessed where the motion artifacts are removed. The four networks are UNet, Segmentation Network (Seg Net), High-Resolution Network (HR Net) and VGG UNet. Trained on our dataset of more than 3,000 images, HR Net was found to be the best performing network achieving an accuracy of 96.24% and a Dice score of 0.9127. The analysis shows that lightweight CNN models perform better than other neural net models when to segment infectious tissue due to COVID-19 from CT slices. Twit Text FOAVip Performance analysis of lightweight CNN models to segment infectious lung tissues of COVID-19 cases from tomographic images ????PeerJ Comput Sci http://www.kidney.de/pget.php?&tw=1&pmid=33817018 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33817018 #FOAvip English Wikipedia <ref>{{Cite pmid|33817018}}</ref> Performance analysis of lightweight CNN models to segment infectious lung tissues of COVID-19 cases from tomographic images #MMPMID33817018 Iyer TJ; Joseph Raj AN; Ghildiyal S; Nersisson R PeerJ Comput Sci 2021[]; 7 (ä): e368 PMID33817018show ga The pandemic of Coronavirus Disease-19 (COVID-19) has spread around the world, causing an existential health crisis. Automated detection of COVID-19 infections in the lungs from Computed Tomography (CT) images offers huge potential in tackling the problem of slow detection and augments the conventional diagnostic procedures. However, segmenting COVID-19 from CT Scans is problematic, due to high variations in the types of infections and low contrast between healthy and infected tissues. While segmenting Lung CT Scans for COVID-19, fast and accurate results are required and furthermore, due to the pandemic, most of the research community has opted for various cloud based servers such as Google Colab, etc. to develop their algorithms. High accuracy can be achieved using Deep Networks but the prediction time would vary as the resources are shared amongst many thus requiring the need to compare different lightweight segmentation model. To address this issue, we aim to analyze the segmentation of COVID-19 using four Convolutional Neural Networks (CNN). The images in our dataset are preprocessed where the motion artifacts are removed. The four networks are UNet, Segmentation Network (Seg Net), High-Resolution Network (HR Net) and VGG UNet. Trained on our dataset of more than 3,000 images, HR Net was found to be the best performing network achieving an accuracy of 96.24% and a Dice score of 0.9127. The analysis shows that lightweight CNN models perform better than other neural net models when to segment infectious tissue due to COVID-19 from CT slices. äPerformance analysis of lightweight CNN models to segment infectious l(epmc).pdf DeepDyve Pubget Overpricing