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10.1016/j.patrec.2020.10.001 http://scihub22266oqcxt.onion/10.1016/j.patrec.2020.10.001 33041409!7532353!33041409     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Pattern+Recognit+Lett 2020 ; 140 (ä): 95-100 Nephropedia Template TP {{tp|p=33041409|t=2020. A light CNN for detecting COVID-19 from CT scans of the chest |pdf=|usr=}}{{33041409}} gab.com Text A light CNN for detecting COVID-19 from CT scans of the chest JO: Pattern Recognit Lett http://www.kidney.de/pget.php?&tw=1&pmid=33041409 #FOAvip Computer Tomography (CT) imaging of the chest is a valid diagnosis tool to detect COVID-19 promptly and to control the spread of the disease. In this work we propose a light Convolutional Neural Network (CNN) design, based on the model of the SqueezeNet, for the efficient discrimination of COVID-19 CT images with respect to other community-acquired pneumonia and/or healthy CT images. The architecture allows to an accuracy of 85.03% with an improvement of about 3.2% in the first dataset arrangement and of about 2.1% in the second dataset arrangement. The obtained gain, though of low entity, can be really important in medical diagnosis and, in particular, for Covid-19 scenario. Also the average classification time on a high-end workstation, 1.25 s, is very competitive with respect to that of more complex CNN designs, 13.41 s, witch require pre-processing. The proposed CNN can be executed on medium-end laptop without GPU acceleration in 7.81 s: this is impossible for methods requiring GPU acceleration. The performance of the method can be further improved with efficient pre-processing strategies for witch GPU acceleration is not necessary. Twit Text FOAVip A light CNN for detecting COVID-19 from CT scans of the chest ????Pattern Recognit Lett http://www.kidney.de/pget.php?&tw=1&pmid=33041409 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33041409 #FOAvip English Wikipedia <ref>{{Cite pmid|33041409}}</ref> A light CNN for detecting COVID-19 from CT scans of the chest #MMPMID33041409 Polsinelli M; Cinque L; Placidi G Pattern Recognit Lett 2020[Dec]; 140 (ä): 95-100 PMID33041409show ga Computer Tomography (CT) imaging of the chest is a valid diagnosis tool to detect COVID-19 promptly and to control the spread of the disease. In this work we propose a light Convolutional Neural Network (CNN) design, based on the model of the SqueezeNet, for the efficient discrimination of COVID-19 CT images with respect to other community-acquired pneumonia and/or healthy CT images. The architecture allows to an accuracy of 85.03% with an improvement of about 3.2% in the first dataset arrangement and of about 2.1% in the second dataset arrangement. The obtained gain, though of low entity, can be really important in medical diagnosis and, in particular, for Covid-19 scenario. Also the average classification time on a high-end workstation, 1.25 s, is very competitive with respect to that of more complex CNN designs, 13.41 s, witch require pre-processing. The proposed CNN can be executed on medium-end laptop without GPU acceleration in 7.81 s: this is impossible for methods requiring GPU acceleration. The performance of the method can be further improved with efficient pre-processing strategies for witch GPU acceleration is not necessary. äA light CNN for detecting COVID-19 from CT scans of the chest (epmc).pdf DeepDyve Pubget Overpricing