Detection of COVID-19 from CT scan images using deep neural networks
8th International Conference on Signal Processing and Integrated Networks, SPIN 2021
; : 390-395, 2021.
Article
in English
| Scopus | ID: covidwho-1752440
ABSTRACT
The coronavirus pandemic brought the world to a standstill of historic significance. Countries over the world have imposed lockdowns, quarantines and travel bans in an effort to stop the further spread of the disease. Healthcare systems worldwide are under extreme pressure due to the influx of a large amount of patients suffering from COVID-19. Moreover, there is a dearth of doctors, nurses, and support staff in hospitals of many countries. In such a predicament, it is imperative to leverage the advances made in computer vision and deep learning technologies to create a system that attempts to ease the burden on worldwide healthcare. In this research, ten state-of-the-art pre-trained convolutional neural networks were used to identify COVID-19 in chest Computed Tomography (CT) scan images. After extensive experimental testing and tuning, comprehensive comparative analysis was done and very promising results were obtained in this classification task. © 2021 IEEE
COVID-19 identification; deep convolutional neural network; deep learning; Image processing; Computerized tomography; Convolution; Convolutional neural networks; Coronavirus; Health care; Medical imaging; Computed tomography scan; Coronaviruses; Extreme pressure; Healthcare systems; Images processing; Large amounts; Patient's suffering; Scan images; Deep neural networks
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
8th International Conference on Signal Processing and Integrated Networks, SPIN 2021
Year:
2021
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS