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Title: Covid-19 Detection From Chest X-Rays Using Modified VGG 16 Model
Authors: Karajah, Eman Naser $AAUP$Palestinian
Awad, Mohammed $AAUP$Palestinian
Keywords: Coronavirus, Covid-19
Deep Learning
Transfer Learning
Visual Geometry Group (VGG 16)
Issue Date: 31-Jan-2022
Publisher: IEEE Xplore
Citation: International Conference on Promising Electronic Technologies (ICPET)
Series/Report no.: (ICPET), 2021;, pp. 46-51
Abstract: Covid-19 is a newly discovered coronavirus that the World Health Organization has officially announced in March 2020 as a pandemic. It is a new virus in the medical field with no specific treatment. Besides, they have not discovered all the symptoms but only some of them. Covid-19 is spreading very fast as the medical systems worldwide cannot hospitalize all the patients, which leads to an increase in the number of the virus death. This work will use the power of deep learning and transfer learning to give faster diagnoses for infection in Covid-19 using X-ray images. The proposed approach is a modified version from Visual Geometry Group (VGG 16). It uses the architecture of the VGG16 with modification to achieve higher accuracy. The model was trained to classify X-ray images into two classes; normal (healthy) and Covid-19 (sick) classes. The model can then predict any uploaded X-ray image class as normal or Covid 19. The achieved accuracy by modified VGG 16 is 99.7%. The model is evaluated through a confusion matrix, precision, accuracy, recall, and f measure.
Appears in Collections:Faculty & Staff Scientific Research publications

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