Impact of Image Augmentation in COVID-19 Detection Using Chest X-Ray Images

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dc.contributor.authorAzade, A.
dc.contributor.authorAnand Kumar, M.
dc.date.accessioned2026-02-06T06:35:39Z
dc.date.issued2022
dc.description.abstractCOVID-19 continues to have a devastating impact on people's lives worldwide. In order to combat this condition, it is critical to test affected people in a timely and cost-effective manner. Radiological examination is one of the most efficient ways to attain this goal, with the most widely available and least expensive alternative being a CXR. The artificial intelligence and data science communities have aided in the global response to COVID-19, a novel coronavirus. Detection and diagnosis techniques have focused on developing rapid diagnostic approaches based on chest X-rays and deep learning. In this paper, we have analyzed the impact of augmentation in COVID-19 CXR images with normal lung opacity and viral pneumonia images and presented a model for the detection of COVID-19. © 2022 IEEE.
dc.identifier.citation2022 IEEE Delhi Section Conference, DELCON 2022, 2022, Vol., , p. -
dc.identifier.urihttps://doi.org/10.1109/DELCON54057.2022.9752785
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/29991
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectAugmentation
dc.subjectConvolution Neural Network
dc.subjectDeep Learning
dc.subjectLung Opacity
dc.subjectRadiography
dc.titleImpact of Image Augmentation in COVID-19 Detection Using Chest X-Ray Images

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