Abstract
According to World Health Organization data, Coronavirus (COVID-19) has infected about 660, 378, 145 patients around the world. It is nonetheless difficult for physicians to detect COVID-19 infections out of CT or X-ray radiographs. Thus, several computer-aided diagnosis (CAD) systems based on deep learning and radiographs were developed to detect COVID-19 infections. However, the majority of approaches considered small datasets, which is ineligible to provide diverse COVID-19 radiographs. This work utilizes a massive number of X-ray radiographs, and compared standard CNN, DenseNet-121, and GoogLeNet for isolating COVID-19 infections out from normal and other pneumonia radiographs. The dataset in this work is large enough to evaluate the realistic performance of those models in labeling COVID-19 infections. Considering the time complexity, accuracy, precision, recall, and F1 score, the experimental results shows that the DenseNet-121 is not only the optimal model, but also there is superior for standard CNN compared to the second output of GoogLeNet, which is an unexplained phenomenon.
Recommended Citation
Talib, Aqeel R. and Ali, Hana’ M.
(2023)
"Deep Learning-based CAD System for Predicting the COVID-19 X-ray Images,"
Karbala International Journal of Modern Science: Vol. 9
:
Iss.
3
, Article 13.
Available at:
https://doi.org/10.33640/2405-609X.3316
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