الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
Biomedical data analysis is playing an important role in health-care provision
services. Recently, there has been a signi cant rise in the amount of biomedical
data collection. As the amount of biomedical data produced every day
increases, the risk of human making analytical and diagnostic mistakes also
increases. Hence, there is a need for the development of fast, accurate, reliable
and automatic means for analysis of biomedical data. To overcome these
limitations, this thesis concerns two case studies. In the rst case study, an
Electroencephalogram (EEG) signals for epileptic seizure diagnosis are considered.
In the second case, two studies are conducted using X-ray, Computed
Tomography (CT) and Ultrasonic (US) images for lung disease diagnosis.
In the rst case study, two proposed approaches are introduced for epileptic
seizure diagnosis. The rst proposed approach depends on Machine Learning
(ML) and some statistical features in Empirical Mode Decomposition
(EMD) domain. The simulation results reveal the e ectiveness of the proposed
approach from the accuracy perspective, but the approach becomes complex as
the number of features increase. In order to overcome this limitation an endto-
end classi cation approach is introduced. The proposed approach depends
on spectrogram and Phase Space Reconstruction (PSR) as a pre-processing
stage to convert the 1-D EEG signals into 2-D images. Di erent Convolutional
Neural Network (CNN) models are used with this strategy. The simulation
results prove that the PSR gives better classi cation results than those given
by the spectrogram. The PSR is a direct projection from the time domain,
which keeps the main trend of di erent signal activities.
In the second case study, ML and di erent deep learning strategies are
introduced for COVID-19 detection from X-ray, CT and US images. First
of all, ML is adopted on features extracted manually from images. Twelve
classi ers are compared for this task. To extend the feasibility of this study,
we have modi ed the features extraction strategy to give deep features. Four
pre-trained models are adopted in this study. Moreover, Transfer Learning (TL) is also introduced in this study to enhance the COVID-19 detection
process. The selected classi cation hierarchy is also compared with a CNN
model built from scratch to prove its quality of classi cation. Simulation
results prove that deep features and TL methods provide the highest accuracy
of 100%.
In addition, an automated approach for diagnosis of lung diseases from
chest X-ray and CT images is introduced. This approach depends on Superresolution
(SR) as a tool for enhancing the details of chest X-ray and CT
images. Deep Learning (DL) algorithms are exploited for the SR task. In
addition, the classi cation process is performed also with DL. Moreover, a
comparison study between softmax and Multi-class Support Vector Machine
(MCSVM) classi ers is presented. The proposed approach is evaluated using
three di erent publicly available X-ray and CT images. Simulation results
reveal that the combination between image SR and InceptionResNetv2 gives
the best classi cation accuracy of 98.028%.