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Classification of CT Scan Images for diagnosis of Covid-19 using Deep Learning
2022 International Mobile and Embedded Technology Conference, MECON 2022 ; : 617-620, 2022.
Article in English | Scopus | ID: covidwho-1840276
ABSTRACT
Coronavirus diseases is a contagious transmissible infectious malady rooted by the SARS-CoV-2 virus and it mostly affects the lungs thereby causing a global health care problem. Coronavirus triggers respiratory tract infection by infecting upper respiratory tract consisting of sinuses, nose, and throat or lower tract of respiratory system that includes windpipe and lungs. WHO proclaimed the COVID-19 outbreak a global epidemic. To control the spreading of novel Coronavirus, early detection and cure is mandatory. Although RT-PCR test is used to detect the infected humans but owing to colossal demand RT-PCR kits are now limited, and its low reliability made way for implementation of radiographic procedures such as X-Rays and Computed Tomography-Scan for symptomatic purposes. These come with a great specificity for diagnosing and detecting Covid-19 instances. This study suggests adopting a Deep Learning technique to automate the diagnosis of COVID19 infection using CT scans of patients for explicit identification of Covid-19. CNN namely Densenet, Inception and Xception networks or architectures are used in a deep learning architecture to distinguish human beings based on whether confirmed positive or not for COVID-19 infection. These networks are then collated on the ground of their accuracy and the outcomes procured from various CNN models are analysed to obtain a robust system. © 2022 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2022 International Mobile and Embedded Technology Conference, MECON 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 2022 International Mobile and Embedded Technology Conference, MECON 2022 Year: 2022 Document Type: Article