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1.
Journal of Electronic Resources Librarianship ; 34(2):121-134, 2022.
Article in English | Taylor & Francis | ID: covidwho-1915355
2.
Comput Math Methods Med ; 2021: 5528144, 2021.
Article in English | MEDLINE | ID: covidwho-1262412

ABSTRACT

Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant's technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnostic imaging , Deep Learning , SARS-CoV-2 , Algorithms , COVID-19/diagnosis , COVID-19 Testing/statistics & numerical data , Computational Biology , Diagnosis, Differential , Humans , Mathematical Concepts , Neural Networks, Computer , Pneumonia, Viral/diagnosis , Pneumonia, Viral/diagnostic imaging , Radiography, Thoracic/statistics & numerical data , Tomography, X-Ray Computed/statistics & numerical data
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