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Please use this identifier to cite or link to this item:

http://hdl.handle.net/20.500.12358/28676
TitleCOVID-19 Detection in X-ray Images using CNN Algorithm
Title in Arabicاكتشاب 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.

Authors
Musleh, Areej A.wahab Ahmed
Maghari, Ashraf Yunis
TypeConference Paper
Date2020-12
LanguageEnglish
Subjects
COVID-19
X-rays
CNN
CheXNet
Classification
Published in2020 International Conference on Promising Electronic Technologies (ICPET)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Citation
Item linkItem Link
DOI10.1109/ICPET51420.2020.00010
ISBN9780738111391
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  • Staff Publications- Faculty of Information Technology [192]
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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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