الفهرس 
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Abstract
This thesis studies deep learning models for analyzing COVID-19 disease. These
models aid in distinguishing patients’ cases such as COVID-19, viral pneumonia, lung
opacity, or healthier with high performance. Deep learning approaches are strongly
encouraged in developing these expert COVID-19 models, which can aid clinicians in the
early diagnosis and prediction of COVID-19 disease. Deep learning models are trained
using a neural network architecture or a set of labeled data that contains multiple
layers. These architectures learn features directly from the data without hindrance to
manual feature extraction [1].
In this thesis, the performance of several COVID-19 imaging approaches is examined
including Chest X-ray (CXR), and Computed Tomography (CT). Medical CT scans and CXR
images are used as inputs to classification methods that emphasize the role of
physiological changes unique to COVID-19. For the analysis of COVID-19 in medical
images, this thesis studies a computer aided design model that can recognize positive
COVID-19 and other lung diseases cases. These demonstrated the pipeline of medical
images and estimation procedures involved in COVID-19 image acquirement and
diagnosis, utilizing Computed Tomography (CT) scans and Chest X-Rays (CXR) images[2].
This thesis studies two deep learning models, namely AlexNet and VGG-16, to detect
cases of COVID-19. These classification approaches are applied to several medical
images to detect COVID-19 and other lung diseases. The two proposed architectures
have achieved a multi-classes classification accuracy of 97.28% and 98.8%, respectively,
for CXR images, while achieving 98.41% and 99.42% accuracy for CT images. Finally, the
results of the two deep models showed better performance compared to other recent
Studies.