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10.1016/j.eswa.2021.115069 http://scihub22266oqcxt.onion/10.1016/j.eswa.2021.115069 33897121!8055948!33897121     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33897121&cmd=llinks): Failed to open stream: HTTP request failed! 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SUFMACS: A machine learning-based robust image segmentation framework for COVID-19 radiological image interpretation |pdf=|usr=}}{{33897121}} gab.com Text SUFMACS: A machine learning-based robust image segmentation framework for COVID-19 radiological image interpretation JO: Expert Syst Appl http://www.kidney.de/pget.php?&tw=1&pmid=33897121 #FOAvip The absence of dedicated vaccines or drugs makes the COVID-19 a global pandemic, and early diagnosis can be an effective prevention mechanism. RT-PCR test is considered as one of the gold standards worldwide to confirm the presence of COVID-19 infection reliably. Radiological images can also be used for the same purpose to some extent. Easy and no contact acquisition of the radiological images makes it a suitable alternative and this work can help to locate and interpret some prominent features for the screening purpose. One major challenge of this domain is the absence of appropriately annotated ground truth data. Motivated from this, a novel unsupervised machine learning-based method called SUFMACS (SUperpixel based Fuzzy Memetic Advanced Cuckoo Search) is proposed to efficiently interpret and segment the COVID-19 radiological images. This approach adapts the superpixel approach to reduce a large amount of spatial information. The original cuckoo search approach is modified and the Luus-Jaakola heuristic method is incorporated with McCulloch's approach. This modified cuckoo search approach is used to optimize the fuzzy modified objective function. This objective function exploits the advantages of the superpixel. Both CT scan and X-ray images are investigated in detail. Both qualitative and quantitative outcomes are quite promising and prove the efficiency and the real-life applicability of the proposed approach. Twit Text FOAVip SUFMACS: A machine learning-based robust image segmentation framework for COVID-19 radiological image interpretation ????Expert Syst Appl http://www.kidney.de/pget.php?&tw=1&pmid=33897121 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33897121 #FOAvip English Wikipedia <ref>{{Cite pmid|33897121}}</ref> SUFMACS: A machine learning-based robust image segmentation framework for COVID-19 radiological image interpretation #MMPMID33897121 Chakraborty S; Mali K Expert Syst Appl 2021[Sep]; 178 (ä): 115069 PMID33897121show ga The absence of dedicated vaccines or drugs makes the COVID-19 a global pandemic, and early diagnosis can be an effective prevention mechanism. RT-PCR test is considered as one of the gold standards worldwide to confirm the presence of COVID-19 infection reliably. Radiological images can also be used for the same purpose to some extent. Easy and no contact acquisition of the radiological images makes it a suitable alternative and this work can help to locate and interpret some prominent features for the screening purpose. One major challenge of this domain is the absence of appropriately annotated ground truth data. Motivated from this, a novel unsupervised machine learning-based method called SUFMACS (SUperpixel based Fuzzy Memetic Advanced Cuckoo Search) is proposed to efficiently interpret and segment the COVID-19 radiological images. This approach adapts the superpixel approach to reduce a large amount of spatial information. The original cuckoo search approach is modified and the Luus-Jaakola heuristic method is incorporated with McCulloch's approach. This modified cuckoo search approach is used to optimize the fuzzy modified objective function. This objective function exploits the advantages of the superpixel. Both CT scan and X-ray images are investigated in detail. Both qualitative and quantitative outcomes are quite promising and prove the efficiency and the real-life applicability of the proposed approach. äSUFMACS: A machine learning-based robust image segmentation framework (epmc).pdf DeepDyve Pubget Overpricing