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Supervised framework for COVID-19 classification and lesion localization from chest CT
Ethiopian Journal of Health Development ; 34(4):236-243, 2020.
Article in English | GIM | ID: covidwho-1300010
ABSTRACT

Background:

Quick and precise identification of people suspected of having COVID-19 plays a key function in imposing quarantine at the right time and providing medical treatment, and results not only in societal benefits but also helps in the development of an improved health system. Building a deep-learning framework for automated identification of COVID-19 using chest computed tomography (CT) is beneficial in tackling the epidemic.

Aim:

To outline a novel deep-learning model created using 3D CT volumes for COVID-19 classification and localization of swellings.
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Collection: Databases of international organizations Database: GIM Language: English Journal: Ethiopian Journal of Health Development Year: 2020 Document Type: Article

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Collection: Databases of international organizations Database: GIM Language: English Journal: Ethiopian Journal of Health Development Year: 2020 Document Type: Article