A new virus disease spread last December in Wuhan city in
China for uncertain reasons and it was named by the World Health Organization
(WHO) as COVID-19. The Coronavirus
disease (COVID-19) has had an incredible influence in the last few years. It
causes thousands of deaths around the world. This makes a rapid research
movement to deal with this new virus. As a computer science, much technical
research has been done to tackle it by using image processing algorithms. This
study was conducted by experimenting on the recent dataset, the Kaggle dataset
of COVID-19 X-ray images, and used the ResNet50 deep learning network with 5
and 10-fold cross-validation. This study introduces a method based on deep
learning networks to classify COVID-19 based on X-ray images. This result is
encouraging to rely on to classify the infected people from the normal. The
experiment results show that 5 folds give more effective results than 10 folds
with an accuracy rate of 97.28%. Henceforth, deep learning can offer
significant results in recognizing the virus in its earliest stages. Future
studies can be conducted on different architectures of deep learning using
different datasets which helps to recognize the infected people in earlier
stages and save their lives.
Author(s) Details:
Ashwan A. Abdulmunem,
College of Computer Science and Information Technology, University
of Kerbala, Iraq.
Zinah Abulridha
Abutiheen,
College of
Computer Science and Information Technology, University of Kerbala, Iraq.
Hiba J. Aleqabie,
College of information Technology (Engineering), Al-Zahraa
University for Women, Kerbala, Iraq.
Please see the link here: https://stm.bookpi.org/RUDHR-V1/article/view/13233
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