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Enhanced COVID-19 Detection in CT Images using Preprocessed Dense Convolutional Neural Networks

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dc.contributor.author Bouguerra, Oussama
dc.contributor.author Bouguerra, Oussama
dc.date.accessioned 2025-03-18T11:52:12Z
dc.date.available 2025-03-18T11:52:12Z
dc.date.issued 2024
dc.identifier.uri http://depot.umc.edu.dz/handle/123456789/14549
dc.description.abstract This study addresses the need for rapid and accurate COVID-19 diagnosis by utilizing Convolutional Neural Networks (CNNs) on CT chest images. Given the limitations of traditional RT-PCR testing, CT imaging has emerged as a highly sensitive diagnostic tool, but manual analysis can be time-consuming and error- prone. This research investigates the impact of data augmentation and median filtering on COVID-19 classification performance using CT images. Experiments were conducted with two pre-trained CNN models DenseNet121 and DenseNet169 across three conditions: (1) COVID with data augmentation, (2) COVID without data augmentation, and (3) COVID with median filter and data augmentation fr_FR
dc.title Enhanced COVID-19 Detection in CT Images using Preprocessed Dense Convolutional Neural Networks fr_FR
dc.type Article fr_FR


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