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- W3165525989 abstract "Abstract Coronavirus is a global emergency as of May 2021. If not acted upon by drugs at the right time, coronavirus may result in the death of individuals. Hence early diagnosis is very important along the progress of the disease. This paper focuses on coronavirus detection using x-ray images, for automating the diagnosis pipeline using convolutional neural networks and transfer learning. This could be deployed in places where radiologists are not easily available in order to detect the disease at very early stages. In this study we propose our deep learning architecture for the classification task, which is trained with modified images, through multiple steps of preprocessing. Our classification method uses convolutional neural networks and transfer learning architecture for classifying the images. Our findings yield an accuracy value of 91.03%, precision of 89.76 %, recall value of 96.67% and F1 score of 93.09%." @default.
- W3165525989 created "2021-06-07" @default.
- W3165525989 creator A5013960740 @default.
- W3165525989 date "2021-05-25" @default.
- W3165525989 modified "2023-10-16" @default.
- W3165525989 title "Deep Covid - Covid Diagnosis Using Deep Neural Networks and Transfer Learning" @default.
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- W3165525989 doi "https://doi.org/10.1101/2021.05.20.21257387" @default.
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