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Digital
Library of the European Council for Modelling and Simulation |
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Title: |
On The Effect Of Decomposition Granularity On DeTraC
For COVID-19 Detection Using Chest X-Ray Images |
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Authors: |
Nicole P. Mugova, Mohammed M. Abdelsamea, Mohamed M. Gaber |
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Published in:
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(2021). ECMS 2021,
35th Proceedings DOI: http://doi.org/10.7148/2021 ISSN:
2522-2422 (ONLINE) ISSN:
2522-2414 (PRINT) ISSN:
2522-2430 (CD-ROM) ISBN: 978-3-937436-72-2 Communications of the ECMS , Volume 35, Issue 1, June 2021, United Kingdom |
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Citation
format: |
Nicole P. Mugova,
Mohammed M. Abdelsamea, Mohamed M. Gaber (2021). On The Effect Of Decomposition Granularity
On DeTraC For COVID-19 Detection Using Chest X-Ray
Images, ECMS 2021 Proceedings Edited By:
Khalid Al-Begain, Mauro Iacono,
Lelio Campanile, Andrzej Bargiela European Council for Modeling and Simulation. doi: 10.7148/2021-0029 |
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DOI: |
https://doi.org/10.7148/2021-0029 |
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Abstract: |
Covid-19 is a
growing issue in society and there is a need for resources to manage the
disease. This paper looks at studying the effect of class decomposition in
our previously proposed deep Convolutional Neural
Network, called DeTraC (Decompose, Transfer and Compose).
DeTraC has the ability to robustly detect and
predict Covid-19 from chest X-ray images. The experimental results showed
that changing the number of clusters affected the performance of DeTraC and influenced the accuracy of the model. As the
number of clusters increased, the accuracy decreased for the shallow tuning
mode but increased for the deep tuning mode. This shows the importance of
using suitable hyperparameter settings in order to
get the best results from a deep learning model. The highest accuracy
obtained, in this study, was 98.33% from the deep tuning model. |
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