Deep Learning Analysis for COVID-19 Using Neural Network Algorithms
Lecture Notes on Data Engineering and Communications Technologies
; 86:313-320, 2022.
Article
in English
| Scopus | ID: covidwho-1739278
ABSTRACT
The COVID-19 pandemic threatens to devastatingly impact the global population’s safety. A successful surveillance of contaminated patients is a crucial move in the battle against COVID-19, and radiological photographs via chest X-ray are one of the main screening strategies. Recent research showed that patients have abnormalities in photographs of chest X-ray that are characteristic of COVID-19 infects. This has inspired a set of deep learning artificial intelligence (AI) programs, and it has been seen that the precision of the identification of COVID-19 contaminated patients utilizing chest X-rays has been quite positive. However, these built AI schemes, to the extent of their author’s awareness, have become closed sources and not accessible for further learning and expansion by the scientific community, so they are not open to the general public. This thesis therefore implements COVID-Net to identify COVID-19 cases of chest X-rays images, an open source, accessible to the general public, a deep neural network architecture adapted to the detection. The COVID-Net data collection, which is referred to as COVIDx which includes 13,800 chest X-ray photographs of 13,725 patients from 3 open-access data sources, one of which we launched, are also addressed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
Lecture Notes on Data Engineering and Communications Technologies
Year:
2022
Document Type:
Article
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