الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
Brain tumor is one of the most challenging health care issues, and hence, it requires the
use of modern technologies in the detection and classification processes. Classifying a
brain tumor requires an accurate and prompt diagnosis of the tumor type because the
selection of successful treatment methods depends mostly on the pathological type.
However, the conventional method for the identification and classification of Magnetic
Resonance Imaging (MRI) brain tumors is through human observation that relies heavily
on the expertise of radiologists who study and interpret image characteristics and
usually give a non-accurate diagnosis. Computer-aided diagnostic methods are highly
desirable for these issues. A brain tumor is an undesirable mass of aberrant brain cells.
There are two types of brain tumors: noncancerous tumors and malignant tumors. Noncancerous
(benign) tumors do not extend to surrounding tissue or organs and grow more
slowly than malignant tumors. Furthermore, cancerous tumors (malignant) are divided
into two types: primary tumors that originate inside the brain and secondary tumors
known as brain metastasis tumors that move from elsewhere. Accurate and timely detection
of brain tumor grade has a serious influence not only on earlier stage brain tumor
diagnosis but also on treatment decisions and tumor growth evaluation for the patient.
The detection technique is considered an essential and obvious process used to identify
brain images of tumors from the available database. Artificial Intelligence (AI) methodologies
can be used to obtain consistent high performance for diagnosing brain tumors.
Among the AI methodologies, Deep learning (DL) networks have gained much
popularity compared to traditional Machine Learning (ML) methods. Magnetic Resonance
(MR) images are the most important medical imaging technique for diagnosing
brain tumors. Deep Convolutional Neural Networks (DCNN) is a special type of neural
network, which can automatically learn representations from the data This dissertation presents different DCNN models for the early detection of brain MRI
images. The first model proposes a classification approach based on CNN architecture
for brain tumor detection from magnetic resonance (MR) images and achieves a clas sification accuracy of 96.05%. Moreover, a CNN model based on SqueezeNet architecture
is suggested for the classification of brain tumor MR images into the normal
and abnormal brain. the proposed model attains an accuracy of 97.78% for augmented
data and 96.35 for nonaugmented dataset.
Furthermore, a DL model based on CNN is accomplished in two different scenarios to
detect tumors. This model can be considered a modified version of the ResNet18 network.
Where, the first scenario is done by applying the brain image directly to the
suggested model. The second scenario presents an IoT-based framework that relies on
a multiuser detection system by sending images to the cloud for the early detection of
brain tumors. The proposed model (In the first Scenario) attaining an accuracy of
98.67%, while the proposed model (In the second Scenario) provided an accuracy of
95.53%.
Finally, an automatic brain tumor detection system based on CNN architecture is proposed
to classify different types of brain tumors using two different datasets. The former
one classifies tumors into (meningioma, glioma, and pituitary tumor). The other
one differentiates between the three glioma grades (Grade II, Grade III, and Grade
IV) and the network structure achieves a significant performance with the best overall
accuracy of 98.2% and 96.5%, respectively.