Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12358/28676
Title | COVID-19 Detection in X-ray Images using CNN Algorithm |
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Title in Arabic | اكتشاب مرض كوفيد-19 في صور الاشعة باستخدام الشبكات العصبية |
Abstract |
Based on the best published research from Stanford University, the CheXNet algorithm was developed to diagnose and detect pneumonia from chest X-rays. To achieve better performance than experienced radiologists from the same university, simple changes were made to the algorithm to diagnose 14 pathological condition in the chest X-ray with a performance that exceeds all Previously developed deep learning [1]. In this paper, we experimented with applying a convolutional neural networks (CNN) algorithm in a similar way to the mechanism of work in CheXNet algorithm by using a dataset of 550 Chest X-ray images collected from Kaggle website, some of them are infected with Covid-19 virus. We had an acceptable prediction accuracy of 89.7% which is closed to the results of CheXNet algorithm. |
Type | Conference Paper |
Date | 2020-12 |
Language | English |
Subjects | |
Published in | 2020 International Conference on Promising Electronic Technologies (ICPET) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Citation | |
Item link | Item Link |
DOI | 10.1109/ICPET51420.2020.00010 |
ISBN | 9780738111391 |
License | ![]() |
Collections | |
Files in this item | ||
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COVID-19 Detection in X-ray Images using-30-11.pdf | 347.6Kb |