Automatic detection of COVID-19 from chest x-ray images using deep learning model
2021 International Conference on Science and Applied Science, ICSAS 2021
; 2424, 2022.
Article
in English
| Scopus | ID: covidwho-1788362
ABSTRACT
The infectious disease caused by novel coronavirus (2019-nCoV) has been widely spreading since last year and has shaken the entire world. It has caused an unprecedented effect on daily life, global economy and public health. Hence this disease detection has life-saving importance for both patients as well as doctors. Due to limited test kits, it is also a daunting task to test every patient with severe respiratory problems using conventional techniques (RT-PCR). Thus implementing an automatic diagnosis system is urgently required to overcome the scarcity problem of Covid-19 test kits at hospital, health care systems. The diagnostic approach is mainly classified into two categories-laboratory based and Chest radiography approach. In this paper, a novel approach for computerized coronavirus (2019-nCoV) detection from lung x-ray images is presented. Here, we propose models using deep learning to show the effectiveness of diagnostic systems. In the experimental result, we evaluate proposed models on publicly available data-set which exhibit satisfactory performance and promising results compared with other previous existing methods. © 2022 Author(s).
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2021 International Conference on Science and Applied Science, ICSAS 2021
Year:
2022
Document Type:
Article
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