An Approach for COVID-19 Identification from Chest X-ray Images Using High-Resolution Networks
2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022
; : 140-144, 2022.
Article
in English
| Scopus | ID: covidwho-2236691
ABSTRACT
In this paper, we present an approach for COVID-19 identification from chest X-ray images by using high-resolution neural networks. These networks allow to connect high-to-low convolution streams in parallel. They can maintain high-resolution representations and generate different resolutions throughout the whole process. The high-resolution based models have shown the superior performance in several applications. The experiments were evaluated on a collection of three data sources containing 24,786 lung X-ray images, which were categorized into three classes including covid pneumonia, non-pneumonia, and viral pneumonia. The proposed approach can attain the overall accuracy of 98.2% and 97.56% for the training and testing set, respectively. The accuracy for each class is 99.37%, 94.83%, and 97.27%, respectively, for non-pneumonia, covid-pneumonia, and viral-pneumonia. © 2022 IEEE.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
Language:
English
Journal:
2022 RIVF International Conference on Computing and Communication Technologies, RIVF 2022
Year:
2022
Document Type:
Article
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