Detection of COVID-19 from CT scan images: A spiking neural network-based approach.
Detection of COVID-19 from CT scan images: A spiking neural network-based approach.
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DOI：
10.1007/s00521-021-05910-1
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发表时间：
2021
影响因子：
6
通讯作者：
Sarkar R
中科院分区：
文献类型：
--
作者：
Garain A;Basu A;Giampaolo F;Velasquez JD;Sarkar R
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.
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DOI：
10.1007/s10489-020-01904-z
发表时间：
2021
期刊：
Applied intelligence (Dordrecht, Netherlands)
影响因子：
--
作者：
Goel T;Murugan R;Mirjalili S;Chakrabartty DK
通讯作者：
Chakrabartty DK
影响因子：
1.6
作者：
Gautrais, J;Thorpe, S
通讯作者：
Thorpe, S
影响因子：
4.3
作者：
Mozafari, Milad;Ganjtabesh, Mohammad;Masquelier, Timothee
通讯作者：
Masquelier, Timothee
影响因子：
1.9
作者：
LANDIS, JR;KOCH, GG
通讯作者：
KOCH, GG
影响因子：
5.3
作者：
Bi, GQ;Poo, MM
通讯作者：
Poo, MM