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10.1007/s00521-021-05910-1 http://scihub22266oqcxt.onion/10.1007/s00521-021-05910-1 33879976!8050640!33879976     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Neural+Comput+Appl 2021 ; 33 (19): 12591-12604 Nephropedia Template TP {{tp|p=33879976|t=2021. Detection of COVID-19 from CT scan images: A spiking neural network-based approach |pdf=|usr=}}{{33879976}} gab.com Text Detection of COVID-19 from CT scan images: A spiking neural network-based approach JO: Neural Comput Appl http://www.kidney.de/pget.php?&tw=1&pmid=33879976 #FOAvip The outbreak of a global pandemic called coronavirus has created unprecedented circumstances resulting into a large number of deaths and risk of community spreading throughout the world. Desperate times have called for desperate measures to detect the disease at an early stage via various medically proven methods like chest computed tomography (CT) scan, chest X-Ray, etc., in order to prevent the virus from spreading across the community. Developing deep learning models for analysing these kinds of radiological images is a well-known methodology in the domain of computer based medical image analysis. However, doing the same by mimicking the biological models and leveraging the newly developed neuromorphic computing chips might be more economical. These chips have been shown to be more powerful and are more efficient than conventional central and graphics processing units. Additionally, these chips facilitate the implementation of spiking neural networks (SNNs) in real-world scenarios. To this end, in this work, we have tried to simulate the SNNs using various deep learning libraries. We have applied them for the classification of chest CT scan images into COVID and non-COVID classes. Our approach has achieved very high F1 score of 0.99 for the potential-based model and outperforms many state-of-the-art models. The working code associated with our present work can be found here. Twit Text FOAVip Detection of COVID-19 from CT scan images: A spiking neural network-based approach ????Neural Comput Appl http://www.kidney.de/pget.php?&tw=1&pmid=33879976 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33879976 #FOAvip English Wikipedia <ref>{{Cite pmid|33879976}}</ref> Detection of COVID-19 from CT scan images: A spiking neural network-based approach #MMPMID33879976 Garain A; Basu A; Giampaolo F; Velasquez JD; Sarkar R Neural Comput Appl 2021[]; 33 (19): 12591-12604 PMID33879976show ga The outbreak of a global pandemic called coronavirus has created unprecedented circumstances resulting into a large number of deaths and risk of community spreading throughout the world. Desperate times have called for desperate measures to detect the disease at an early stage via various medically proven methods like chest computed tomography (CT) scan, chest X-Ray, etc., in order to prevent the virus from spreading across the community. Developing deep learning models for analysing these kinds of radiological images is a well-known methodology in the domain of computer based medical image analysis. However, doing the same by mimicking the biological models and leveraging the newly developed neuromorphic computing chips might be more economical. These chips have been shown to be more powerful and are more efficient than conventional central and graphics processing units. Additionally, these chips facilitate the implementation of spiking neural networks (SNNs) in real-world scenarios. To this end, in this work, we have tried to simulate the SNNs using various deep learning libraries. We have applied them for the classification of chest CT scan images into COVID and non-COVID classes. Our approach has achieved very high F1 score of 0.99 for the potential-based model and outperforms many state-of-the-art models. The working code associated with our present work can be found here. äDetection of COVID-19 from CT scan images: A spiking neural network-ba(epmc).pdf DeepDyve Pubget Overpricing